Untitled
Kartik Nagarajan
December 18, 2019
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## sliceThe data set has 8946 rows and 262 columns Cost(zillow) and Revenue(Airbnb) datasets are first cleaned individually and then joined in order to improve the overall data quality.
Process: I have catered to the main issues with bad quality of data which are:
Missing Values, Duplicated Rows
We can delete rows with having more than 50% missing values and also making sure to check the effect of removing a particular variable by checking its overall variance using statistical modeling in SAS.
We can also use principal component analysis in order to reduce overal variables which wont impact the overall variance by much. Filter imbalanced columns, cater to categorical variables, high missing value columns, columns not associated to the problem statement etc. Analyze any redudant columns and aggregate based on reasoning/assumptions. Check for correlation between variables in order to detect multicollinearity which could be a big problem during statistical modelling.
Produce clean cost and revenue data for joining and further explore the joined data to derive meaningful insights.
cost <- read.csv("C:/Users/Kartik/Airbnb/Zillow_dataset.csv")
#View(cost)
dim(cost)## [1] 8946 262head(cost)revenue <- read.csv("C:/Users/Kartik/Airbnb/airbnbbbbbbb.csv")
#View(revenue)
kable(head(cost)) %>% kable_styling (bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| RegionID | RegionName | City | State | Metro | CountyName | SizeRank | X1996.04 | X1996.05 | X1996.06 | X1996.07 | X1996.08 | X1996.09 | X1996.10 | X1996.11 | X1996.12 | X1997.01 | X1997.02 | X1997.03 | X1997.04 | X1997.05 | X1997.06 | X1997.07 | X1997.08 | X1997.09 | X1997.10 | X1997.11 | X1997.12 | X1998.01 | X1998.02 | X1998.03 | X1998.04 | X1998.05 | X1998.06 | X1998.07 | X1998.08 | X1998.09 | X1998.10 | X1998.11 | X1998.12 | X1999.01 | X1999.02 | X1999.03 | X1999.04 | X1999.05 | X1999.06 | X1999.07 | X1999.08 | X1999.09 | X1999.10 | X1999.11 | X1999.12 | X2000.01 | X2000.02 | X2000.03 | X2000.04 | X2000.05 | X2000.06 | X2000.07 | X2000.08 | X2000.09 | X2000.10 | X2000.11 | X2000.12 | X2001.01 | X2001.02 | X2001.03 | X2001.04 | X2001.05 | X2001.06 | X2001.07 | X2001.08 | X2001.09 | X2001.10 | X2001.11 | X2001.12 | X2002.01 | X2002.02 | X2002.03 | X2002.04 | X2002.05 | X2002.06 | X2002.07 | X2002.08 | X2002.09 | X2002.10 | X2002.11 | X2002.12 | X2003.01 | X2003.02 | X2003.03 | X2003.04 | X2003.05 | X2003.06 | X2003.07 | X2003.08 | X2003.09 | X2003.10 | X2003.11 | X2003.12 | X2004.01 | X2004.02 | X2004.03 | X2004.04 | X2004.05 | X2004.06 | X2004.07 | X2004.08 | X2004.09 | X2004.10 | X2004.11 | X2004.12 | X2005.01 | X2005.02 | X2005.03 | X2005.04 | X2005.05 | X2005.06 | X2005.07 | X2005.08 | X2005.09 | X2005.10 | X2005.11 | X2005.12 | X2006.01 | X2006.02 | X2006.03 | X2006.04 | X2006.05 | X2006.06 | X2006.07 | X2006.08 | X2006.09 | X2006.10 | X2006.11 | X2006.12 | X2007.01 | X2007.02 | X2007.03 | X2007.04 | X2007.05 | X2007.06 | X2007.07 | X2007.08 | X2007.09 | X2007.10 | X2007.11 | X2007.12 | X2008.01 | X2008.02 | X2008.03 | X2008.04 | X2008.05 | X2008.06 | X2008.07 | X2008.08 | X2008.09 | X2008.10 | X2008.11 | X2008.12 | X2009.01 | X2009.02 | X2009.03 | X2009.04 | X2009.05 | X2009.06 | X2009.07 | X2009.08 | X2009.09 | X2009.10 | X2009.11 | X2009.12 | X2010.01 | X2010.02 | X2010.03 | X2010.04 | X2010.05 | X2010.06 | X2010.07 | X2010.08 | X2010.09 | X2010.10 | X2010.11 | X2010.12 | X2011.01 | X2011.02 | X2011.03 | X2011.04 | X2011.05 | X2011.06 | X2011.07 | X2011.08 | X2011.09 | X2011.10 | X2011.11 | X2011.12 | X2012.01 | X2012.02 | X2012.03 | X2012.04 | X2012.05 | X2012.06 | X2012.07 | X2012.08 | X2012.09 | X2012.10 | X2012.11 | X2012.12 | X2013.01 | X2013.02 | X2013.03 | X2013.04 | X2013.05 | X2013.06 | X2013.07 | X2013.08 | X2013.09 | X2013.10 | X2013.11 | X2013.12 | X2014.01 | X2014.02 | X2014.03 | X2014.04 | X2014.05 | X2014.06 | X2014.07 | X2014.08 | X2014.09 | X2014.10 | X2014.11 | X2014.12 | X2015.01 | X2015.02 | X2015.03 | X2015.04 | X2015.05 | X2015.06 | X2015.07 | X2015.08 | X2015.09 | X2015.10 | X2015.11 | X2015.12 | X2016.01 | X2016.02 | X2016.03 | X2016.04 | X2016.05 | X2016.06 | X2016.07 | X2016.08 | X2016.09 | X2016.10 | X2016.11 | X2016.12 | X2017.01 | X2017.02 | X2017.03 | X2017.04 | X2017.05 | X2017.06 |
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| 61639 | 10025 | New York | NY | New York | New York | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 798600 | 798800 | 801500 | 804600 | 814900 | 828300 | 835700 | 849300 | 858100 | 854000 | 834800 | 821700 | 830300 | 853700 | 868300 | 875200 | 882200 | 892400 | 905000 | 924000 | 934400 | 932100 | 927500 | 923600 | 907900 | 890900 | 883400 | 896100 | 923900 | 952900 | 964600 | 972500 | 973800 | 973400 | 966500 | 966800 | 967100 | 974800 | 976800 | 976100 | 973700 | 974500 | 973200 | 966400 | 950400 | 933300 | 920900 | 909400 | 891400 | 873300 | 858800 | 850200 | 842800 | 834000 | 828800 | 821400 | 813900 | 813300 | 821500 | 831700 | 845100 | 854500 | 858900 | 859200 | 863500 | 876000 | 886100 | 890000 | 894200 | 901800 | 909500 | 913300 | 907400 | 900000 | 897700 | 896300 | 892300 | 890400 | 888600 | 891700 | 899500 | 904400 | 908200 | 914000 | 915100 | 912300 | 914000 | 921100 | 923300 | 917300 | 915000 | 922800 | 929100 | 937700 | 955700 | 974200 | 995500 | 1019500 | 1035100 | 1054900 | 1079900 | 1092600 | 1103500 | 1118800 | 1139300 | 1154600 | 1144100 | 1120300 | 1125500 | 1136000 | 1135100 | 1130000 | 1138200 | 1153700 | 1174800 | 1185400 | 1188400 | 1189700 | 1193700 | 1199900 | 1201400 | 1202600 | 1214200 | 1235200 | 1258000 | 1287700 | 1307200 | 1313900 | 1317100 | 1327400 | 1338800 | 1350400 | 1356600 | 1358500 | 1364000 | 1373300 | 1382600 | 1374400 | 1364100 | 1366300 | 1354800 | 1327500 | 1317300 | 1333700 | 1352100 | 1390000 | 1431000 |
| 84654 | 60657 | Chicago | IL | Chicago | Cook | 2 | 167700 | 166400 | 166700 | 167200 | 166900 | 166900 | 168000 | 170100 | 171700 | 173000 | 174600 | 177600 | 180100 | 182300 | 184400 | 186300 | 187600 | 189400 | 190300 | 189700 | 189800 | 191900 | 194500 | 195500 | 196000 | 196900 | 198900 | 201400 | 204600 | 207900 | 211800 | 214600 | 216000 | 217500 | 220200 | 222800 | 226200 | 229600 | 232400 | 234400 | 236300 | 238300 | 241800 | 246100 | 249500 | 251300 | 253200 | 255700 | 259200 | 263100 | 266600 | 269500 | 272800 | 275500 | 278800 | 283400 | 288600 | 291300 | 292400 | 294600 | 297100 | 298200 | 299800 | 302000 | 304200 | 307900 | 311000 | 311400 | 311000 | 311700 | 312300 | 312000 | 311800 | 312600 | 313000 | 314400 | 317300 | 319700 | 320500 | 321000 | 321600 | 323800 | 326100 | 329000 | 332200 | 334700 | 336000 | 337300 | 337500 | 337100 | 334900 | 333100 | 332800 | 333400 | 335100 | 337800 | 339400 | 340700 | 343200 | 345000 | 345200 | 344600 | 344500 | 346200 | 349800 | 353400 | 355100 | 356300 | 358300 | 359500 | 359200 | 358500 | 359100 | 361400 | 364700 | 367500 | 369400 | 369800 | 369600 | 368700 | 368100 | 369000 | 370300 | 371700 | 374200 | 375700 | 376400 | 378200 | 379800 | 380100 | 380400 | 381200 | 382700 | 382700 | 380200 | 377800 | 376300 | 375600 | 376000 | 376200 | 375800 | 376300 | 377200 | 376800 | 373700 | 370100 | 368700 | 368600 | 366600 | 362200 | 358600 | 355300 | 352300 | 350900 | 350100 | 347900 | 345400 | 343400 | 342800 | 342600 | 341100 | 339900 | 338900 | 338200 | 335200 | 329800 | 325500 | 323600 | 323400 | 325000 | 325800 | 323200 | 320100 | 318600 | 317400 | 315700 | 315000 | 315300 | 315600 | 313900 | 309800 | 305700 | 301800 | 299500 | 299900 | 301100 | 300300 | 298900 | 298500 | 298500 | 297000 | 296800 | 298700 | 299600 | 300700 | 303900 | 306800 | 307500 | 308500 | 310000 | 310800 | 311200 | 313000 | 315800 | 319000 | 323400 | 327500 | 330000 | 331800 | 334500 | 336000 | 335700 | 335400 | 336300 | 338800 | 342400 | 344400 | 344000 | 343900 | 345100 | 346100 | 346900 | 348000 | 349700 | 351200 | 351700 | 350700 | 350400 | 352000 | 354300 | 355900 | 356500 | 355200 | 353800 | 353700 | 354600 | 356200 | 357800 | 358200 | 358500 | 360300 | 362400 | 363700 | 365200 | 367100 | 368600 | 370200 | 372300 | 375300 | 378700 | 381400 | 381800 | 382100 | 383300 | 385100 |
| 61637 | 10023 | New York | NY | New York | New York | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1526800 | 1424500 | 1346600 | 1331300 | 1322500 | 1289300 | 1265400 | 1249700 | 1241100 | 1232700 | 1225500 | 1228200 | 1252600 | 1266100 | 1288700 | 1308100 | 1333000 | 1356400 | 1362000 | 1353600 | 1364000 | 1373900 | 1389600 | 1401600 | 1404100 | 1415800 | 1432400 | 1455400 | 1474200 | 1462300 | 1438300 | 1435500 | 1427800 | 1411200 | 1407400 | 1419700 | 1457400 | 1500800 | 1524900 | 1537800 | 1558700 | 1586100 | 1602300 | 1621100 | 1639300 | 1657400 | 1657400 | 1656100 | 1649400 | 1643400 | 1632400 | 1618200 | 1588300 | 1543600 | 1500800 | 1464200 | 1426100 | 1387300 | 1362600 | 1351700 | 1344300 | 1331800 | 1334800 | 1314200 | 1271900 | 1252300 | 1262300 | 1279200 | 1309000 | 1335300 | 1353800 | 1366400 | 1372100 | 1381300 | 1385000 | 1388100 | 1399100 | 1399800 | 1389300 | 1384700 | 1380900 | 1367900 | 1365400 | 1375100 | 1380400 | 1377000 | 1375100 | 1379000 | 1395200 | 1414500 | 1419000 | 1403100 | 1383200 | 1376700 | 1378200 | 1378700 | 1375900 | 1366700 | 1365500 | 1382200 | 1404700 | 1428000 | 1445700 | 1452900 | 1460100 | 1484400 | 1508400 | 1522800 | 1538300 | 1568600 | 1597400 | 1622900 | 1654300 | 1684600 | 1713000 | 1728800 | 1736100 | 1745900 | 1753800 | 1736600 | 1730400 | 1734500 | 1728700 | 1720800 | 1717700 | 1700100 | 1680400 | 1676400 | 1685600 | 1708100 | 1730400 | 1751800 | 1778300 | 1810400 | 1831600 | 1844400 | 1861600 | 1889600 | 1901500 | 1895300 | 1890200 | 1898400 | 1924500 | 1967300 | 1993500 | 1980700 | 1960900 | 1951300 | 1937800 | 1929800 | 1955000 | 2022400 | 2095000 | 2142300 |
| 84616 | 60614 | Chicago | IL | Chicago | Cook | 4 | 195800 | 193500 | 192600 | 192300 | 192600 | 193600 | 195500 | 197600 | 199400 | 201300 | 203600 | 206500 | 209200 | 211100 | 212600 | 214400 | 215600 | 216500 | 217900 | 220100 | 222200 | 223900 | 225400 | 227700 | 230100 | 231700 | 232700 | 233700 | 234700 | 235600 | 236800 | 238800 | 240800 | 242400 | 243800 | 246400 | 250200 | 254300 | 257600 | 261100 | 264800 | 267900 | 270700 | 272800 | 274400 | 276200 | 278600 | 280100 | 283100 | 287700 | 293600 | 298500 | 302700 | 305000 | 306800 | 309400 | 313100 | 314900 | 316200 | 318200 | 320600 | 322900 | 325500 | 328400 | 330700 | 332800 | 334400 | 335900 | 337400 | 339700 | 342300 | 343800 | 343400 | 342300 | 341800 | 341700 | 342400 | 344300 | 346900 | 348900 | 350200 | 351700 | 353500 | 355700 | 358000 | 361600 | 364000 | 365500 | 366400 | 367000 | 365200 | 363100 | 362000 | 362500 | 364800 | 368200 | 370800 | 371400 | 371200 | 371800 | 374200 | 377800 | 380100 | 381700 | 384400 | 386600 | 385700 | 385400 | 388500 | 392200 | 394500 | 394900 | 394800 | 395500 | 400000 | 404300 | 407600 | 409500 | 410400 | 408700 | 406400 | 403800 | 402400 | 402300 | 403000 | 403100 | 403600 | 404500 | 406100 | 407700 | 408700 | 409600 | 409800 | 408700 | 408600 | 411000 | 412200 | 411400 | 410200 | 408400 | 406600 | 406500 | 406400 | 404600 | 402900 | 400600 | 397500 | 392600 | 387500 | 383700 | 381000 | 378900 | 377700 | 377400 | 376700 | 374000 | 369600 | 366200 | 364500 | 364100 | 364300 | 363600 | 361900 | 361900 | 356400 | 347100 | 342400 | 344000 | 345200 | 346900 | 346100 | 342600 | 340100 | 339900 | 338600 | 335500 | 333900 | 335000 | 336000 | 334200 | 330300 | 327000 | 326000 | 326100 | 326700 | 326300 | 324400 | 322700 | 323200 | 322800 | 320700 | 319500 | 320100 | 320500 | 321800 | 323600 | 324300 | 324100 | 324700 | 326000 | 327600 | 329800 | 332600 | 336800 | 342300 | 348100 | 353600 | 358900 | 361900 | 363900 | 366200 | 368300 | 369800 | 371400 | 372400 | 373200 | 373800 | 374800 | 376200 | 376800 | 376300 | 374900 | 373800 | 373900 | 374700 | 375300 | 375000 | 374700 | 376300 | 378100 | 378000 | 377700 | 378300 | 380000 | 383100 | 385900 | 388100 | 389700 | 391800 | 393400 | 394700 | 394900 | 395700 | 396400 | 397500 | 398900 | 401200 | 403200 | 405700 | 408300 | 408800 | 408000 | 410100 | 412200 | 412200 |
| 93144 | 79936 | El Paso | TX | El Paso | El Paso | 5 | 59100 | 60500 | 60900 | 60800 | 60300 | 60400 | 61200 | 61700 | 61000 | 60100 | 59300 | 59000 | 58700 | 58400 | 58000 | 57800 | 57900 | 57800 | 57800 | 58100 | 58400 | 58700 | 59200 | 59400 | 58700 | 58100 | 57900 | 57900 | 57700 | 57600 | 57500 | 57800 | 58000 | 58000 | 57900 | 57800 | 57800 | 58000 | 58500 | 58700 | 59000 | 59200 | 59300 | 59500 | 59900 | 60300 | 60400 | 60300 | 60300 | 60000 | 59500 | 59400 | 59800 | 59900 | 59700 | 59500 | 59400 | 59500 | 59700 | 59700 | 59200 | 58700 | 58400 | 57800 | 57000 | 56700 | 56800 | 56600 | 56500 | 56500 | 56600 | 56700 | 56600 | 56700 | 57000 | 57300 | 57300 | 57300 | 57100 | 56900 | 56900 | 57000 | 56700 | 56700 | 57000 | 57400 | 57700 | 58000 | 58300 | 58600 | 58800 | 59100 | 59500 | 60200 | 61100 | 61700 | 62400 | 63100 | 63600 | 63800 | 64400 | 64900 | 65100 | 65200 | 65300 | 65600 | 66300 | 67100 | 67900 | 68800 | 69600 | 70600 | 71500 | 72100 | 72600 | 73400 | 74200 | 74500 | 75000 | 75700 | 76400 | 77100 | 77700 | 78100 | 78700 | 79600 | 80600 | 81500 | 82300 | 83200 | 83900 | 84400 | 84300 | 84400 | 85200 | 86100 | 86800 | 87400 | 87700 | 87800 | 88300 | 88800 | 88500 | 87900 | 87500 | 86600 | 85500 | 84600 | 84200 | 83900 | 83600 | 83400 | 83500 | 84100 | 84600 | 84600 | 84700 | 84600 | 84600 | 85100 | 85500 | 85800 | 86600 | 87600 | 86500 | 84200 | 83300 | 83800 | 83800 | 83600 | 83600 | 83400 | 82900 | 82400 | 82200 | 82100 | 82300 | 82800 | 82900 | 82600 | 82700 | 83100 | 83300 | 83100 | 82800 | 82500 | 82300 | 82200 | 82300 | 82200 | 81900 | 81700 | 82100 | 82600 | 82900 | 83000 | 83000 | 82900 | 82100 | 81200 | 80800 | 80700 | 81200 | 81800 | 81800 | 81400 | 81400 | 81500 | 81900 | 82000 | 81900 | 81900 | 82100 | 82100 | 81500 | 80800 | 80300 | 80100 | 80100 | 80700 | 81200 | 81700 | 81900 | 81700 | 81500 | 81700 | 81700 | 80900 | 81000 | 81500 | 81400 | 80500 | 80000 | 80100 | 80500 | 80800 | 81400 | 82300 | 82600 | 82600 | 82500 | 82500 | 82600 | 82700 | 82600 | 82400 | 82300 | 82400 | 82300 | 82500 | 83200 | 83900 | 84100 | 83900 | 83700 |
| 84640 | 60640 | Chicago | IL | Chicago | Cook | 6 | 123300 | 122600 | 122000 | 121500 | 120900 | 120600 | 120900 | 121300 | 121600 | 122100 | 122900 | 124200 | 125300 | 126100 | 126700 | 127900 | 129300 | 130400 | 131300 | 131700 | 132300 | 133500 | 134500 | 134800 | 135200 | 135500 | 136300 | 137700 | 139600 | 141600 | 143400 | 144500 | 145600 | 147400 | 149200 | 149900 | 150500 | 151700 | 152800 | 153900 | 156400 | 159400 | 161800 | 163800 | 165700 | 167100 | 168400 | 169600 | 171000 | 172400 | 174800 | 177900 | 180400 | 182300 | 184600 | 187700 | 190700 | 193100 | 196100 | 200000 | 202900 | 205500 | 207600 | 208600 | 209200 | 210000 | 210200 | 211300 | 213000 | 214100 | 215200 | 217600 | 220200 | 222400 | 224600 | 227000 | 228600 | 230100 | 232400 | 234300 | 235300 | 236200 | 237000 | 237900 | 239200 | 241300 | 243600 | 244200 | 243800 | 244500 | 245500 | 245700 | 246400 | 247500 | 248000 | 249000 | 251100 | 252900 | 253700 | 256100 | 259400 | 261300 | 262200 | 263800 | 265300 | 267600 | 270500 | 272800 | 273700 | 273600 | 273200 | 273300 | 273500 | 273800 | 274300 | 274400 | 274400 | 275300 | 276600 | 278200 | 280600 | 283100 | 284400 | 284200 | 283000 | 282000 | 282400 | 283700 | 285200 | 286700 | 286700 | 284900 | 283300 | 282700 | 282100 | 280800 | 280300 | 280200 | 280100 | 279900 | 281200 | 282300 | 282100 | 279300 | 275800 | 273100 | 272600 | 271800 | 270300 | 268600 | 266900 | 264800 | 262900 | 260700 | 258500 | 256600 | 255000 | 252900 | 250600 | 248900 | 248900 | 250100 | 250300 | 250400 | 248000 | 243800 | 240200 | 239700 | 239000 | 238200 | 236800 | 235400 | 233300 | 231300 | 230300 | 228200 | 225200 | 223100 | 222300 | 220700 | 218300 | 215900 | 213200 | 210900 | 209600 | 208200 | 205900 | 204000 | 203200 | 202500 | 200800 | 199400 | 199900 | 201900 | 204500 | 207000 | 208100 | 207100 | 205300 | 204700 | 204700 | 205800 | 208600 | 211800 | 213500 | 213800 | 215100 | 218400 | 221500 | 223900 | 226100 | 227900 | 229100 | 230200 | 230600 | 230400 | 230000 | 229000 | 227600 | 226100 | 225700 | 226200 | 226500 | 226500 | 227100 | 227800 | 229400 | 231800 | 234100 | 235400 | 235100 | 233900 | 233700 | 235300 | 237200 | 238500 | 239300 | 239600 | 239500 | 240200 | 242700 | 244900 | 247700 | 249500 | 248800 | 247000 | 247300 | 248700 | 250800 | 252800 | 253800 | 253800 | 253400 | 254100 | 255100 |
#cost
#head(cost)Missing Values Median Price for early years (1996-2014) has plenty of Null values as shown in the table below. This is not consistent across all Regionnames. In order to cater this we have to make sure to handle this null values in a more effective manner.
This data showcases the number of missing vlaues for each of the variables. Seeing this table helps us in understanding quality of each variables and implement methods for variable selection and feature engineering.
numMissingVal <-sapply(cost, function(x) sum(length(which(is.na(x)))))
#numMissingVal
colnames(cost)## [1] "RegionID" "RegionName" "City" "State" "Metro"
## [6] "CountyName" "SizeRank" "X1996.04" "X1996.05" "X1996.06"
## [11] "X1996.07" "X1996.08" "X1996.09" "X1996.10" "X1996.11"
## [16] "X1996.12" "X1997.01" "X1997.02" "X1997.03" "X1997.04"
## [21] "X1997.05" "X1997.06" "X1997.07" "X1997.08" "X1997.09"
## [26] "X1997.10" "X1997.11" "X1997.12" "X1998.01" "X1998.02"
## [31] "X1998.03" "X1998.04" "X1998.05" "X1998.06" "X1998.07"
## [36] "X1998.08" "X1998.09" "X1998.10" "X1998.11" "X1998.12"
## [41] "X1999.01" "X1999.02" "X1999.03" "X1999.04" "X1999.05"
## [46] "X1999.06" "X1999.07" "X1999.08" "X1999.09" "X1999.10"
## [51] "X1999.11" "X1999.12" "X2000.01" "X2000.02" "X2000.03"
## [56] "X2000.04" "X2000.05" "X2000.06" "X2000.07" "X2000.08"
## [61] "X2000.09" "X2000.10" "X2000.11" "X2000.12" "X2001.01"
## [66] "X2001.02" "X2001.03" "X2001.04" "X2001.05" "X2001.06"
## [71] "X2001.07" "X2001.08" "X2001.09" "X2001.10" "X2001.11"
## [76] "X2001.12" "X2002.01" "X2002.02" "X2002.03" "X2002.04"
## [81] "X2002.05" "X2002.06" "X2002.07" "X2002.08" "X2002.09"
## [86] "X2002.10" "X2002.11" "X2002.12" "X2003.01" "X2003.02"
## [91] "X2003.03" "X2003.04" "X2003.05" "X2003.06" "X2003.07"
## [96] "X2003.08" "X2003.09" "X2003.10" "X2003.11" "X2003.12"
## [101] "X2004.01" "X2004.02" "X2004.03" "X2004.04" "X2004.05"
## [106] "X2004.06" "X2004.07" "X2004.08" "X2004.09" "X2004.10"
## [111] "X2004.11" "X2004.12" "X2005.01" "X2005.02" "X2005.03"
## [116] "X2005.04" "X2005.05" "X2005.06" "X2005.07" "X2005.08"
## [121] "X2005.09" "X2005.10" "X2005.11" "X2005.12" "X2006.01"
## [126] "X2006.02" "X2006.03" "X2006.04" "X2006.05" "X2006.06"
## [131] "X2006.07" "X2006.08" "X2006.09" "X2006.10" "X2006.11"
## [136] "X2006.12" "X2007.01" "X2007.02" "X2007.03" "X2007.04"
## [141] "X2007.05" "X2007.06" "X2007.07" "X2007.08" "X2007.09"
## [146] "X2007.10" "X2007.11" "X2007.12" "X2008.01" "X2008.02"
## [151] "X2008.03" "X2008.04" "X2008.05" "X2008.06" "X2008.07"
## [156] "X2008.08" "X2008.09" "X2008.10" "X2008.11" "X2008.12"
## [161] "X2009.01" "X2009.02" "X2009.03" "X2009.04" "X2009.05"
## [166] "X2009.06" "X2009.07" "X2009.08" "X2009.09" "X2009.10"
## [171] "X2009.11" "X2009.12" "X2010.01" "X2010.02" "X2010.03"
## [176] "X2010.04" "X2010.05" "X2010.06" "X2010.07" "X2010.08"
## [181] "X2010.09" "X2010.10" "X2010.11" "X2010.12" "X2011.01"
## [186] "X2011.02" "X2011.03" "X2011.04" "X2011.05" "X2011.06"
## [191] "X2011.07" "X2011.08" "X2011.09" "X2011.10" "X2011.11"
## [196] "X2011.12" "X2012.01" "X2012.02" "X2012.03" "X2012.04"
## [201] "X2012.05" "X2012.06" "X2012.07" "X2012.08" "X2012.09"
## [206] "X2012.10" "X2012.11" "X2012.12" "X2013.01" "X2013.02"
## [211] "X2013.03" "X2013.04" "X2013.05" "X2013.06" "X2013.07"
## [216] "X2013.08" "X2013.09" "X2013.10" "X2013.11" "X2013.12"
## [221] "X2014.01" "X2014.02" "X2014.03" "X2014.04" "X2014.05"
## [226] "X2014.06" "X2014.07" "X2014.08" "X2014.09" "X2014.10"
## [231] "X2014.11" "X2014.12" "X2015.01" "X2015.02" "X2015.03"
## [236] "X2015.04" "X2015.05" "X2015.06" "X2015.07" "X2015.08"
## [241] "X2015.09" "X2015.10" "X2015.11" "X2015.12" "X2016.01"
## [246] "X2016.02" "X2016.03" "X2016.04" "X2016.05" "X2016.06"
## [251] "X2016.07" "X2016.08" "X2016.09" "X2016.10" "X2016.11"
## [256] "X2016.12" "X2017.01" "X2017.02" "X2017.03" "X2017.04"
## [261] "X2017.05" "X2017.06"colnames(revenue)## [1] "id"
## [2] "listing_url"
## [3] "scrape_id"
## [4] "last_scraped"
## [5] "name"
## [6] "summary"
## [7] "space"
## [8] "description"
## [9] "experiences_offered"
## [10] "neighborhood_overview"
## [11] "notes"
## [12] "transit"
## [13] "access"
## [14] "interaction"
## [15] "house_rules"
## [16] "thumbnail_url"
## [17] "medium_url"
## [18] "picture_url"
## [19] "xl_picture_url"
## [20] "host_id"
## [21] "host_url"
## [22] "host_name"
## [23] "host_since"
## [24] "host_location"
## [25] "host_about"
## [26] "host_response_time"
## [27] "host_response_rate"
## [28] "host_acceptance_rate"
## [29] "host_is_superhost"
## [30] "host_thumbnail_url"
## [31] "host_picture_url"
## [32] "host_neighbourhood"
## [33] "host_listings_count"
## [34] "host_total_listings_count"
## [35] "host_verifications"
## [36] "host_has_profile_pic"
## [37] "host_identity_verified"
## [38] "street"
## [39] "neighbourhood"
## [40] "neighbourhood_cleansed"
## [41] "neighbourhood_group_cleansed"
## [42] "city"
## [43] "state"
## [44] "zipcode"
## [45] "market"
## [46] "smart_location"
## [47] "country_code"
## [48] "country"
## [49] "latitude"
## [50] "longitude"
## [51] "is_location_exact"
## [52] "property_type"
## [53] "room_type"
## [54] "accommodates"
## [55] "bathrooms"
## [56] "bedrooms"
## [57] "beds"
## [58] "bed_type"
## [59] "amenities"
## [60] "square_feet"
## [61] "price"
## [62] "weekly_price"
## [63] "monthly_price"
## [64] "security_deposit"
## [65] "cleaning_fee"
## [66] "guests_included"
## [67] "extra_people"
## [68] "minimum_nights"
## [69] "maximum_nights"
## [70] "minimum_minimum_nights"
## [71] "maximum_minimum_nights"
## [72] "minimum_maximum_nights"
## [73] "maximum_maximum_nights"
## [74] "minimum_nights_avg_ntm"
## [75] "maximum_nights_avg_ntm"
## [76] "calendar_updated"
## [77] "has_availability"
## [78] "availability_30"
## [79] "availability_60"
## [80] "availability_90"
## [81] "availability_365"
## [82] "calendar_last_scraped"
## [83] "number_of_reviews"
## [84] "number_of_reviews_ltm"
## [85] "first_review"
## [86] "last_review"
## [87] "review_scores_rating"
## [88] "review_scores_accuracy"
## [89] "review_scores_cleanliness"
## [90] "review_scores_checkin"
## [91] "review_scores_communication"
## [92] "review_scores_location"
## [93] "review_scores_value"
## [94] "requires_license"
## [95] "license"
## [96] "jurisdiction_names"
## [97] "instant_bookable"
## [98] "is_business_travel_ready"
## [99] "cancellation_policy"
## [100] "require_guest_profile_picture"
## [101] "require_guest_phone_verification"
## [102] "calculated_host_listings_count"
## [103] "calculated_host_listings_count_entire_homes"
## [104] "calculated_host_listings_count_private_rooms"
## [105] "calculated_host_listings_count_shared_rooms"
## [106] "reviews_per_month"kable(as.data.frame(numMissingVal)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| numMissingVal | |
|---|---|
| RegionID | 0 |
| RegionName | 0 |
| City | 0 |
| State | 0 |
| Metro | 0 |
| CountyName | 0 |
| SizeRank | 0 |
| X1996.04 | 2662 |
| X1996.05 | 2582 |
| X1996.06 | 2582 |
| X1996.07 | 2577 |
| X1996.08 | 2576 |
| X1996.09 | 2576 |
| X1996.10 | 2576 |
| X1996.11 | 2566 |
| X1996.12 | 2566 |
| X1997.01 | 2542 |
| X1997.02 | 2113 |
| X1997.03 | 2093 |
| X1997.04 | 2093 |
| X1997.05 | 2093 |
| X1997.06 | 2091 |
| X1997.07 | 2091 |
| X1997.08 | 1994 |
| X1997.09 | 1991 |
| X1997.10 | 1991 |
| X1997.11 | 1988 |
| X1997.12 | 1984 |
| X1998.01 | 1967 |
| X1998.02 | 1822 |
| X1998.03 | 1821 |
| X1998.04 | 1865 |
| X1998.05 | 1973 |
| X1998.06 | 1961 |
| X1998.07 | 1776 |
| X1998.08 | 1742 |
| X1998.09 | 1742 |
| X1998.10 | 1730 |
| X1998.11 | 1709 |
| X1998.12 | 1707 |
| X1999.01 | 1706 |
| X1999.02 | 1689 |
| X1999.03 | 1688 |
| X1999.04 | 1688 |
| X1999.05 | 1685 |
| X1999.06 | 1675 |
| X1999.07 | 1675 |
| X1999.08 | 1652 |
| X1999.09 | 1652 |
| X1999.10 | 1652 |
| X1999.11 | 1652 |
| X1999.12 | 1652 |
| X2000.01 | 1652 |
| X2000.02 | 1633 |
| X2000.03 | 1633 |
| X2000.04 | 1631 |
| X2000.05 | 1631 |
| X2000.06 | 1631 |
| X2000.07 | 1626 |
| X2000.08 | 1598 |
| X2000.09 | 1598 |
| X2000.10 | 1598 |
| X2000.11 | 1598 |
| X2000.12 | 1598 |
| X2001.01 | 1679 |
| X2001.02 | 1580 |
| X2001.03 | 1553 |
| X2001.04 | 1552 |
| X2001.05 | 1552 |
| X2001.06 | 1551 |
| X2001.07 | 1551 |
| X2001.08 | 1546 |
| X2001.09 | 1546 |
| X2001.10 | 1546 |
| X2001.11 | 1545 |
| X2001.12 | 1545 |
| X2002.01 | 1545 |
| X2002.02 | 1538 |
| X2002.03 | 1538 |
| X2002.04 | 1537 |
| X2002.05 | 1537 |
| X2002.06 | 1537 |
| X2002.07 | 1537 |
| X2002.08 | 1524 |
| X2002.09 | 1522 |
| X2002.10 | 1521 |
| X2002.11 | 1521 |
| X2002.12 | 1521 |
| X2003.01 | 1520 |
| X2003.02 | 1499 |
| X2003.03 | 1498 |
| X2003.04 | 1498 |
| X2003.05 | 1498 |
| X2003.06 | 1498 |
| X2003.07 | 1498 |
| X2003.08 | 1485 |
| X2003.09 | 1484 |
| X2003.10 | 1483 |
| X2003.11 | 1482 |
| X2003.12 | 1480 |
| X2004.01 | 1479 |
| X2004.02 | 1447 |
| X2004.03 | 1428 |
| X2004.04 | 1428 |
| X2004.05 | 1428 |
| X2004.06 | 1426 |
| X2004.07 | 1426 |
| X2004.08 | 1403 |
| X2004.09 | 1398 |
| X2004.10 | 1392 |
| X2004.11 | 1389 |
| X2004.12 | 1389 |
| X2005.01 | 1388 |
| X2005.02 | 1321 |
| X2005.03 | 1305 |
| X2005.04 | 1303 |
| X2005.05 | 1303 |
| X2005.06 | 1303 |
| X2005.07 | 1303 |
| X2005.08 | 1288 |
| X2005.09 | 1288 |
| X2005.10 | 1286 |
| X2005.11 | 1285 |
| X2005.12 | 1285 |
| X2006.01 | 1284 |
| X2006.02 | 1260 |
| X2006.03 | 1260 |
| X2006.04 | 1259 |
| X2006.05 | 1259 |
| X2006.06 | 1259 |
| X2006.07 | 1259 |
| X2006.08 | 1250 |
| X2006.09 | 1249 |
| X2006.10 | 1248 |
| X2006.11 | 1248 |
| X2006.12 | 1248 |
| X2007.01 | 1247 |
| X2007.02 | 1242 |
| X2007.03 | 1240 |
| X2007.04 | 1239 |
| X2007.05 | 1239 |
| X2007.06 | 1238 |
| X2007.07 | 1238 |
| X2007.08 | 1226 |
| X2007.09 | 1226 |
| X2007.10 | 1226 |
| X2007.11 | 1226 |
| X2007.12 | 1226 |
| X2008.01 | 1226 |
| X2008.02 | 1225 |
| X2008.03 | 1225 |
| X2008.04 | 1225 |
| X2008.05 | 1225 |
| X2008.06 | 1225 |
| X2008.07 | 1225 |
| X2008.08 | 1194 |
| X2008.09 | 1194 |
| X2008.10 | 1194 |
| X2008.11 | 1194 |
| X2008.12 | 1194 |
| X2009.01 | 1193 |
| X2009.02 | 1190 |
| X2009.03 | 1184 |
| X2009.04 | 1178 |
| X2009.05 | 1178 |
| X2009.06 | 1146 |
| X2009.07 | 1146 |
| X2009.08 | 1095 |
| X2009.09 | 1095 |
| X2009.10 | 1095 |
| X2009.11 | 1094 |
| X2009.12 | 1091 |
| X2010.01 | 1091 |
| X2010.02 | 1082 |
| X2010.03 | 971 |
| X2010.04 | 959 |
| X2010.05 | 944 |
| X2010.06 | 923 |
| X2010.07 | 903 |
| X2010.08 | 177 |
| X2010.09 | 177 |
| X2010.10 | 174 |
| X2010.11 | 174 |
| X2010.12 | 174 |
| X2011.01 | 163 |
| X2011.02 | 151 |
| X2011.03 | 149 |
| X2011.04 | 149 |
| X2011.05 | 149 |
| X2011.06 | 147 |
| X2011.07 | 134 |
| X2011.08 | 121 |
| X2011.09 | 120 |
| X2011.10 | 120 |
| X2011.11 | 120 |
| X2011.12 | 118 |
| X2012.01 | 108 |
| X2012.02 | 93 |
| X2012.03 | 91 |
| X2012.04 | 91 |
| X2012.05 | 91 |
| X2012.06 | 87 |
| X2012.07 | 84 |
| X2012.08 | 67 |
| X2012.09 | 66 |
| X2012.10 | 65 |
| X2012.11 | 65 |
| X2012.12 | 65 |
| X2013.01 | 64 |
| X2013.02 | 31 |
| X2013.03 | 26 |
| X2013.04 | 21 |
| X2013.05 | 21 |
| X2013.06 | 20 |
| X2013.07 | 20 |
| X2013.08 | 0 |
| X2013.09 | 0 |
| X2013.10 | 0 |
| X2013.11 | 0 |
| X2013.12 | 0 |
| X2014.01 | 0 |
| X2014.02 | 0 |
| X2014.03 | 0 |
| X2014.04 | 0 |
| X2014.05 | 0 |
| X2014.06 | 0 |
| X2014.07 | 0 |
| X2014.08 | 0 |
| X2014.09 | 0 |
| X2014.10 | 0 |
| X2014.11 | 0 |
| X2014.12 | 0 |
| X2015.01 | 0 |
| X2015.02 | 0 |
| X2015.03 | 0 |
| X2015.04 | 0 |
| X2015.05 | 0 |
| X2015.06 | 0 |
| X2015.07 | 0 |
| X2015.08 | 0 |
| X2015.09 | 0 |
| X2015.10 | 0 |
| X2015.11 | 0 |
| X2015.12 | 0 |
| X2016.01 | 0 |
| X2016.02 | 18 |
| X2016.03 | 18 |
| X2016.04 | 18 |
| X2016.05 | 18 |
| X2016.06 | 0 |
| X2016.07 | 0 |
| X2016.08 | 0 |
| X2016.09 | 0 |
| X2016.10 | 0 |
| X2016.11 | 0 |
| X2016.12 | 3 |
| X2017.01 | 0 |
| X2017.02 | 0 |
| X2017.03 | 0 |
| X2017.04 | 0 |
| X2017.05 | 0 |
| X2017.06 | 0 |
# to check non negative values
costCharCol <- colnames(cost %>% ungroup() %>% select_if(is.character))
#costCharCol
costZeroNeg <- sapply(cost[, !(names(cost) %in% costCharCol)], function(x)
count(x <= 0, na.rm = TRUE))## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factorskable(as.data.frame(costZeroNeg)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| costZeroNeg | |
|---|---|
| RegionID | 0 |
| RegionName | 0 |
| City | 0 |
| State | 0 |
| Metro | 0 |
| CountyName | 0 |
| SizeRank | 0 |
| X1996.04 | 0 |
| X1996.05 | 0 |
| X1996.06 | 0 |
| X1996.07 | 0 |
| X1996.08 | 0 |
| X1996.09 | 0 |
| X1996.10 | 0 |
| X1996.11 | 0 |
| X1996.12 | 0 |
| X1997.01 | 0 |
| X1997.02 | 0 |
| X1997.03 | 0 |
| X1997.04 | 0 |
| X1997.05 | 0 |
| X1997.06 | 0 |
| X1997.07 | 0 |
| X1997.08 | 0 |
| X1997.09 | 0 |
| X1997.10 | 0 |
| X1997.11 | 0 |
| X1997.12 | 0 |
| X1998.01 | 0 |
| X1998.02 | 0 |
| X1998.03 | 0 |
| X1998.04 | 0 |
| X1998.05 | 0 |
| X1998.06 | 0 |
| X1998.07 | 0 |
| X1998.08 | 0 |
| X1998.09 | 0 |
| X1998.10 | 0 |
| X1998.11 | 0 |
| X1998.12 | 0 |
| X1999.01 | 0 |
| X1999.02 | 0 |
| X1999.03 | 0 |
| X1999.04 | 0 |
| X1999.05 | 0 |
| X1999.06 | 0 |
| X1999.07 | 0 |
| X1999.08 | 0 |
| X1999.09 | 0 |
| X1999.10 | 0 |
| X1999.11 | 0 |
| X1999.12 | 0 |
| X2000.01 | 0 |
| X2000.02 | 0 |
| X2000.03 | 0 |
| X2000.04 | 0 |
| X2000.05 | 0 |
| X2000.06 | 0 |
| X2000.07 | 0 |
| X2000.08 | 0 |
| X2000.09 | 0 |
| X2000.10 | 0 |
| X2000.11 | 0 |
| X2000.12 | 0 |
| X2001.01 | 0 |
| X2001.02 | 0 |
| X2001.03 | 0 |
| X2001.04 | 0 |
| X2001.05 | 0 |
| X2001.06 | 0 |
| X2001.07 | 0 |
| X2001.08 | 0 |
| X2001.09 | 0 |
| X2001.10 | 0 |
| X2001.11 | 0 |
| X2001.12 | 0 |
| X2002.01 | 0 |
| X2002.02 | 0 |
| X2002.03 | 0 |
| X2002.04 | 0 |
| X2002.05 | 0 |
| X2002.06 | 0 |
| X2002.07 | 0 |
| X2002.08 | 0 |
| X2002.09 | 0 |
| X2002.10 | 0 |
| X2002.11 | 0 |
| X2002.12 | 0 |
| X2003.01 | 0 |
| X2003.02 | 0 |
| X2003.03 | 0 |
| X2003.04 | 0 |
| X2003.05 | 0 |
| X2003.06 | 0 |
| X2003.07 | 0 |
| X2003.08 | 0 |
| X2003.09 | 0 |
| X2003.10 | 0 |
| X2003.11 | 0 |
| X2003.12 | 0 |
| X2004.01 | 0 |
| X2004.02 | 0 |
| X2004.03 | 0 |
| X2004.04 | 0 |
| X2004.05 | 0 |
| X2004.06 | 0 |
| X2004.07 | 0 |
| X2004.08 | 0 |
| X2004.09 | 0 |
| X2004.10 | 0 |
| X2004.11 | 0 |
| X2004.12 | 0 |
| X2005.01 | 0 |
| X2005.02 | 0 |
| X2005.03 | 0 |
| X2005.04 | 0 |
| X2005.05 | 0 |
| X2005.06 | 0 |
| X2005.07 | 0 |
| X2005.08 | 0 |
| X2005.09 | 0 |
| X2005.10 | 0 |
| X2005.11 | 0 |
| X2005.12 | 0 |
| X2006.01 | 0 |
| X2006.02 | 0 |
| X2006.03 | 0 |
| X2006.04 | 0 |
| X2006.05 | 0 |
| X2006.06 | 0 |
| X2006.07 | 0 |
| X2006.08 | 0 |
| X2006.09 | 0 |
| X2006.10 | 0 |
| X2006.11 | 0 |
| X2006.12 | 0 |
| X2007.01 | 0 |
| X2007.02 | 0 |
| X2007.03 | 0 |
| X2007.04 | 0 |
| X2007.05 | 0 |
| X2007.06 | 0 |
| X2007.07 | 0 |
| X2007.08 | 0 |
| X2007.09 | 0 |
| X2007.10 | 0 |
| X2007.11 | 0 |
| X2007.12 | 0 |
| X2008.01 | 0 |
| X2008.02 | 0 |
| X2008.03 | 0 |
| X2008.04 | 0 |
| X2008.05 | 0 |
| X2008.06 | 0 |
| X2008.07 | 0 |
| X2008.08 | 0 |
| X2008.09 | 0 |
| X2008.10 | 0 |
| X2008.11 | 0 |
| X2008.12 | 0 |
| X2009.01 | 0 |
| X2009.02 | 0 |
| X2009.03 | 0 |
| X2009.04 | 0 |
| X2009.05 | 0 |
| X2009.06 | 0 |
| X2009.07 | 0 |
| X2009.08 | 0 |
| X2009.09 | 0 |
| X2009.10 | 0 |
| X2009.11 | 0 |
| X2009.12 | 0 |
| X2010.01 | 0 |
| X2010.02 | 0 |
| X2010.03 | 0 |
| X2010.04 | 0 |
| X2010.05 | 0 |
| X2010.06 | 0 |
| X2010.07 | 0 |
| X2010.08 | 0 |
| X2010.09 | 0 |
| X2010.10 | 0 |
| X2010.11 | 0 |
| X2010.12 | 0 |
| X2011.01 | 0 |
| X2011.02 | 0 |
| X2011.03 | 0 |
| X2011.04 | 0 |
| X2011.05 | 0 |
| X2011.06 | 0 |
| X2011.07 | 0 |
| X2011.08 | 0 |
| X2011.09 | 0 |
| X2011.10 | 0 |
| X2011.11 | 0 |
| X2011.12 | 0 |
| X2012.01 | 0 |
| X2012.02 | 0 |
| X2012.03 | 0 |
| X2012.04 | 0 |
| X2012.05 | 0 |
| X2012.06 | 0 |
| X2012.07 | 0 |
| X2012.08 | 0 |
| X2012.09 | 0 |
| X2012.10 | 0 |
| X2012.11 | 0 |
| X2012.12 | 0 |
| X2013.01 | 0 |
| X2013.02 | 0 |
| X2013.03 | 0 |
| X2013.04 | 0 |
| X2013.05 | 0 |
| X2013.06 | 0 |
| X2013.07 | 0 |
| X2013.08 | 0 |
| X2013.09 | 0 |
| X2013.10 | 0 |
| X2013.11 | 0 |
| X2013.12 | 0 |
| X2014.01 | 0 |
| X2014.02 | 0 |
| X2014.03 | 0 |
| X2014.04 | 0 |
| X2014.05 | 0 |
| X2014.06 | 0 |
| X2014.07 | 0 |
| X2014.08 | 0 |
| X2014.09 | 0 |
| X2014.10 | 0 |
| X2014.11 | 0 |
| X2014.12 | 0 |
| X2015.01 | 0 |
| X2015.02 | 0 |
| X2015.03 | 0 |
| X2015.04 | 0 |
| X2015.05 | 0 |
| X2015.06 | 0 |
| X2015.07 | 0 |
| X2015.08 | 0 |
| X2015.09 | 0 |
| X2015.10 | 0 |
| X2015.11 | 0 |
| X2015.12 | 0 |
| X2016.01 | 0 |
| X2016.02 | 0 |
| X2016.03 | 0 |
| X2016.04 | 0 |
| X2016.05 | 0 |
| X2016.06 | 0 |
| X2016.07 | 0 |
| X2016.08 | 0 |
| X2016.09 | 0 |
| X2016.10 | 0 |
| X2016.11 | 0 |
| X2016.12 | 0 |
| X2017.01 | 0 |
| X2017.02 | 0 |
| X2017.03 | 0 |
| X2017.04 | 0 |
| X2017.05 | 0 |
| X2017.06 | 0 |
# In order to find duplicate rows in the dataset
cost[which(duplicated(cost) ==T),] #Selecting the city in the dataset
cost <- cost %>% filter(City == "New York")
cost$City## [1] New York New York New York New York New York New York New York
## [8] New York New York New York New York New York New York New York
## [15] New York New York New York New York New York New York New York
## [22] New York New York New York New York
## 4684 Levels: Aberdeen Abilene Abingdon Abington Acton Acushnet ... Zumbro Falls#Creating a list of the years
yearList <- as.character(as.list(2005:2017))
yearList## [1] "2005" "2006" "2007" "2008" "2009" "2010" "2011" "2012" "2013" "2014"
## [11] "2015" "2016" "2017"#calculating the median years
cost1<- cost[,-c(1,3,4,5,6,7)]
medianYears <- data.frame()
medianYearsmedianYearCol <- cost %>% select(starts_with('X2005'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2005, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2006'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2006, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2007'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2007, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2008'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2008, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2009'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2009, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2010'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2010, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2011'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2011, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2012'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2012, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2013'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2013, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2014'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2014, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2015'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2015, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2016'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2016, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)medianYearCol <- cost %>% select(starts_with('X2017'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
medianYearCol$year<- rep(2017, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)
head(medianYearCol)head(medianYears)Median Price Change Over 5 Years Upper half(Orange) of line graph highlights regionNames with significant increase in the median cost price. With RegionName = 10003, 10014 & 10011 median cost price increasing by 0.5 Mn in a matter of 5 years (2013-2017).
Lower half of the plot sees little or no increase. In further sections, each zipcode will further analyzed to reason out these trends.
str(cost$RegionName)## int [1:25] 10025 10023 10128 10011 10003 11201 11234 10314 11215 10028 ...RegionName <- as.numeric(cost$RegionName)
medianPriceAll <-
medianYears %>% filter(year > 2012) %>% ggplot(aes(
x = year,
y = Median,
group = RegionName,
colour = RegionName
)) + geom_line() + geom_point() + scale_y_continuous(labels = scales::comma)
ggplotly(medianPriceAll)Median Price Change Over 2 Years Growth/Increase in median cost price measured for recent 2 consecutive years (2016,2017) shares a different story. The market has been dry with little or no fluctuation. Median Cost for regionName - 10003 which had an upward trend for over 4-5 years (2013-2016) has dropped by 0.1 mn. Rest of the regionNames have little or no increase.
medianPriceAll <-
medianYears %>% filter(year > (2014)) %>% ggplot(aes(
x = year,
y = Median,
group = RegionName,
colour = RegionName
)) + geom_line() + geom_point() + scale_y_continuous(labels = scales::comma)
ggplotly(medianPriceAll)Clean Cost Data From the above analysis, it is safe to assume that Median Cost Price for Homes have rather not a significant increment the past 2 years.To reduce the complexicity of data in hand, median cost price for year (2017) is chosen to be the actual price of the individual
2 bedroom properties across every regionName(zipcode) is filtered based on the requirement. Decisions made here on will be based on this assumption. Choosing the Last One Year Median Price
currentMedianPrice <- medianYears %>% filter(year == 2017)
cost <-
cost %>% select(RegionName, CountyName, SizeRank) %>% inner_join(currentMedianPrice, by = "RegionName")
cost <- rename(cost, cost = Median)
kable(head(cost)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| RegionName | CountyName | SizeRank | cost | year |
|---|---|---|---|---|
| 10025 | New York | 1 | 1342900 | 2017 |
| 10023 | New York | 3 | 1988700 | 2017 |
| 10128 | New York | 14 | 1622500 | 2017 |
| 10011 | New York | 15 | 2354000 | 2017 |
| 10003 | New York | 21 | 2005500 | 2017 |
| 11201 | Kings | 32 | 1400200 | 2017 |
Revenue Data (Airbnb Data) Revenue data contains a mix of information including details about the properties like address, bedrooms, zipcode ,bathrooms to information about host, daily/weekly and monthly price details for stay.
kable(head(revenue)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| id | listing_url | scrape_id | last_scraped | name | summary | space | description | experiences_offered | neighborhood_overview | notes | transit | access | interaction | house_rules | thumbnail_url | medium_url | picture_url | xl_picture_url | host_id | host_url | host_name | host_since | host_location | host_about | host_response_time | host_response_rate | host_acceptance_rate | host_is_superhost | host_thumbnail_url | host_picture_url | host_neighbourhood | host_listings_count | host_total_listings_count | host_verifications | host_has_profile_pic | host_identity_verified | street | neighbourhood | neighbourhood_cleansed | neighbourhood_group_cleansed | city | state | zipcode | market | smart_location | country_code | country | latitude | longitude | is_location_exact | property_type | room_type | accommodates | bathrooms | bedrooms | beds | bed_type | amenities | square_feet | price | weekly_price | monthly_price | security_deposit | cleaning_fee | guests_included | extra_people | minimum_nights | maximum_nights | minimum_minimum_nights | maximum_minimum_nights | minimum_maximum_nights | maximum_maximum_nights | minimum_nights_avg_ntm | maximum_nights_avg_ntm | calendar_updated | has_availability | availability_30 | availability_60 | availability_90 | availability_365 | calendar_last_scraped | number_of_reviews | number_of_reviews_ltm | first_review | last_review | review_scores_rating | review_scores_accuracy | review_scores_cleanliness | review_scores_checkin | review_scores_communication | review_scores_location | review_scores_value | requires_license | license | jurisdiction_names | instant_bookable | is_business_travel_ready | cancellation_policy | require_guest_profile_picture | require_guest_phone_verification | calculated_host_listings_count | calculated_host_listings_count_entire_homes | calculated_host_listings_count_private_rooms | calculated_host_listings_count_shared_rooms | reviews_per_month |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2539 | https://www.airbnb.com/rooms/2539 | 2.019071e+13 | 2019-07-09 | Clean & quiet apt home by the park | Renovated apt home in elevator building. | Spacious, renovated, and clean apt home, one block to F train, 25 minutes to lower Manhatten | Renovated apt home in elevator building. Spacious, renovated, and clean apt home, one block to F train, 25 minutes to lower Manhatten Close to Prospect Park and Historic Ditmas Park Very close to F and G trains and Express bus into NY. The B and Q are closeby also. If this room is unavailable on your desired dates, check out our other rooms, such as: https://www.airbnb.com/rooms/10267242 | none | Close to Prospect Park and Historic Ditmas Park | If this room is unavailable on your desired dates, check out our other rooms, such as: https://www.airbnb.com/rooms/10267242 | Very close to F and G trains and Express bus into NY. The B and Q are closeby also. | -The security and comfort of all our guests is important to us! Therefore, no one is permitted to check in without first emailing a clear government issued photo ID, acceptable to manager. Instructions will be provided in the house manual. -No eating, drinking or storage of food in the room. -No smoking. -Illicit drug use is strictly forbidden. -Quiet hours after 10pm and before 8am, This is respectful for our neighbors and other guests. -Please clean up after yourself when using the kitchen and bath. -Please lock the doors and close the windows when exiting the home. -Please indicate and pay for the correct number of guests. Failure to do so will cancel your reservation with no refund due. -Please remove your shoes when entering the apt home. Thanks for choosing our home! | NA | NA | https://a0.muscache.com/im/pictures/3949d073-a02e-4ebc-aa9c-ac74f00eaa1f.jpg?aki_policy=large | NA | 2787 | https://www.airbnb.com/users/show/2787 | John | 2008-09-07 | New York, New York, United States | Educated professional living in Brooklyn. I love meeting new people, running, hiking, fine foods, traveling, etc. One of my favorite trips was spending New Year’s Eve in London on the Thames River. Big Ben, spectacular fireworks and light show; and fun times with a good crowd of international tourists. A most memorable night and trip! Also, I generally approach life with a positive attitude. I look forward to meeting you. | within an hour | 100% | N/A | f | https://a0.muscache.com/im/pictures/8674565a-758d-476b-a580-4d99ea9baab9.jpg?aki_policy=profile_small | https://a0.muscache.com/im/pictures/8674565a-758d-476b-a580-4d99ea9baab9.jpg?aki_policy=profile_x_medium | Gravesend | 6 | 6 | [‘email’, ‘phone’, ‘reviews’, ‘kba’] | t | t | Brooklyn , NY, United States | Brooklyn | Kensington | Brooklyn | Brooklyn | NY | 11218 | New York | Brooklyn , NY | US | United States | 40.64749 | -73.97237 | f | Apartment | Private room | 2 | 1 | 1 | 1 | Real Bed | {TV,“Cable TV”,Internet,Wifi,“Wheelchair accessible”,Kitchen,“Free parking on premises”,Elevator,“Free street parking”,“Buzzer/wireless intercom”,Heating,“Suitable for events”,Washer,Dryer,“Smoke detector”,“Carbon monoxide detector”,“First aid kit”,“Safety card”,“Fire extinguisher”,Essentials,Shampoo,“24-hour check-in”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”,“Self check-in”,Keypad,“Outlet covers”,“Hot water”,“Bed linens”,“Extra pillows and blankets”,Microwave,“Coffee maker”,Refrigerator,“Dishes and silverware”,“Cooking basics”,Oven,Stove,“Luggage dropoff allowed”,“Long term stays allowed”,“Cleaning before checkout”} | NA | $149.00 | $299.00 | $999.00 | $100.00 | $25.00 | 1 | $35.00 | 1 | 730 | 1 | 1 | 730 | 730 | 1 | 730 | 3 weeks ago | t | 30 | 60 | 90 | 365 | 2019-07-09 | 9 | 2 | 2015-12-04 | 2018-10-19 | 98 | 10 | 10 | 10 | 10 | 10 | 10 | f | f | f | moderate | f | f | 6 | 0 | 5 | 1 | 0.21 | ||||
| 2595 | https://www.airbnb.com/rooms/2595 | 2.019071e+13 | 2019-07-09 | Skylit Midtown Castle | Find your romantic getaway to this beautiful, spacious skylit studio in the heart of Midtown, Manhattan. STUNNING SKYLIT STUDIO / 1 BED + SINGLE / FULL BATH / FULL KITCHEN / FIREPLACE / CENTRALLY LOCATED / WiFi + APPLE TV / SHEETS + TOWELS |
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Find your romantic getaway to this beautiful, spacious skylit studio in the heart of Midtown, Manhattan. STUNNING SKYLIT STUDIO / 1 BED + SINGLE / FULL BATH / FULL KITCHEN / FIREPLACE / CENTRALLY LOCATED / WiFi + APPLE TV / SHEETS + TOWELS - Spacious (500+ft²), immaculate and nicely furnished & designed studio. - Tuck yourself into the ultra comfortable bed under the skylight. Fall in love with a myriad of bright lights in the city night sky. - Single-sized bed/convertible floor mattress with luxury bedding (available upon request). - Gorgeous pyramid skylight with amazing diffused natural light, stunning architectural details, soaring high vaulted ceilings, exposed brick, wood burning fireplace, floor seating area with natural zafu cushions, modern style mixed with eclectic art & antique treasures, large full bath, newly renovated kitchen, air conditioning/heat, high speed WiFi Internet, and Apple TV. - Centrally located in the heart of Midtown Manhattan just a few blocks from all s | none | Centrally located in the heart of Manhattan just a few blocks from all subway connections in the very desirable Midtown location a few minutes walk to Times Square, the Theater District, Bryant Park and Herald Square. | Apartment is located on 37th Street between 5th & 6th Avenue, just a few blocks from all subway connections. Closest Subways (in order of proximity to apartment (Website hidden by Airbnb) W: 34th Street & 6th Avenu (Website hidden by Airbnb) 3: 34th Street & 7th Avenue 7: 42nd & 5th Avenu (Website hidden by Airbnb) S: 42nd Street between Park & Lexington Avenue (Website hidden by Airbnb) E: 34th Street and 8th Avenue If coming by car, there is a parking garage on the block and free street parking. | Guests have full access to the kitchen, bathroom and living spaces. The closets are private/off limits. | I am a Sound Therapy Practitioner and Kundalini Yoga & Meditation teacher. I work with energy and sound for relaxation and healing, using Symphonic gong, singing bowls, tuning forks, drums, voice and other instruments. Sound relaxation sessions and/or personalized Kundalini Yoga sessions are available in the space upon request. Individual, couples or group sessions available. Licensed acupuncture and massage also available upon request. Please inquire. I welcome my guests at the apartment for check-in, or alternatively, a self check-in can be arranged. Once you are settled in, I am just a phone call, text or email away, should you have any questions, concerns or issues during your stay. My desire is that you have a smooth arrival and amazing stay here. | Make yourself at home, respect the space and the neighbors. No pets, no smoking and no unauthorized guests. | NA | NA | https://a0.muscache.com/im/pictures/f0813a11-40b2-489e-8217-89a2e1637830.jpg?aki_policy=large | NA | 2845 | https://www.airbnb.com/users/show/2845 | Jennifer | 2008-09-09 | New York, New York, United States | A New Yorker since 2000! My passion is creating beautiful, unique spaces where unforgettable memories are made. It’s my pleasure to host people from around the world and meet new faces. Welcome travelers! I am a Sound Therapy Practitioner and Kundalini Yoga & Meditation teacher. I work with energy and sound for relaxation and healing, using Symphonic gong, singing bowls, tuning forks, drums, voice and other instruments. | within a few hours | 87% | N/A | f | https://a0.muscache.com/im/users/2845/profile_pic/1259095067/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/2845/profile_pic/1259095067/original.jpg?aki_policy=profile_x_medium | Midtown | 5 | 5 | [‘email’, ‘phone’, ‘reviews’, ‘kba’, ‘work_email’] | t | t | New York, NY, United States | Manhattan | Midtown | Manhattan | New York | NY | 10018 | New York | New York, NY | US | United States | 40.75362 | -73.98377 | f | Apartment | Entire home/apt | 2 | 1 | 0 | 1 | Real Bed | {TV,Wifi,“Air conditioning”,Kitchen,“Paid parking off premises”,“Free street parking”,“Indoor fireplace”,Heating,“Family/kid friendly”,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,“Lock on bedroom door”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“Self check-in”,Keypad,“Private living room”,Bathtub,“Hot water”,“Bed linens”,“Extra pillows and blankets”,“Ethernet connection”,“Coffee maker”,Refrigerator,“Dishes and silverware”,“Cooking basics”,Oven,Stove,“Luggage dropoff allowed”,“Long term stays allowed”,“Cleaning before checkout”,“Wide entrance for guests”,“Flat path to guest entrance”,“Well-lit path to entrance”,“No stairs or steps to enter”} | NA | $225.00 | $1,995.00 | $350.00 | $100.00 | 2 | $0.00 | 1 | 1125 | 1 | 1 | 1125 | 1125 | 1 | 1125 | 4 days ago | t | 25 | 55 | 80 | 355 | 2019-07-09 | 45 | 11 | 2009-11-21 | 2019-05-21 | 95 | 10 | 9 | 10 | 10 | 10 | 9 | f | f | f | strict_14_with_grace_period | t | t | 2 | 1 | 0 | 1 | 0.38 | ||||
| 3647 | https://www.airbnb.com/rooms/3647 | 2.019071e+13 | 2019-07-08 | THE VILLAGE OF HARLEM….NEW YORK ! | WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY This Spacious 1 bedroom is with Plenty of Windows with a View……. Sleeps…..Four Adults…..two in the Livingrm. with (2) Sofa-beds. (Website hidden by Airbnb) two in the Bedrm.on a very Comfortable Queen Size Bed… A Complete Bathrm…..With Shower and Bathtub……. Fully Equipped with Linens & Towels…….. Spacious Living Room……Flat ScreenTelevision…..DVD Player with Movies available for your viewing during your stay………………………………………………………………….. Dining Area…..for Morning Coffee or Tea…………………………………………….. The Kitchen Area is Modern with Granite Counter Top… includes the use of a Coffee Maker…Microwave to Heat up a Carry Out/In Meal…. Not suited for a Gourmet Cook…or Top Chef……Sorry!!!! . This Flat is located in HISTORIC HARLEM…. near the Appollo Theater and The Museum Mile…on Fifth Avenue. Sylvia’s World Famous Resturant…loca | WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY This Spacious 1 bedroom is with Plenty of Windows with a View……. Sleeps…..Four Adults…..two in the Livingrm. with (2) Sofa-beds. (Website hidden by Airbnb) two in the Bedrm.on a very Comfortable Queen Size Bed… A Complete Bathrm…..With Shower and Bathtub……. Fully Equipped with Linens & Towels…….. Spacious Living Room……Flat ScreenTelevision…..DVD Player with Movies available for your viewing during your stay………………………………………………………………….. Dining Area…..for Morning Coffee or Tea…………………………………………….. The Kitchen Area is Modern with Granite Counter Top… includes the use of a Coffee Maker…Microwave to Heat up a Carry Out/In Meal…. Not suited for a Gourmet Cook…or Top Chef……Sorry!!!! . This Flat is located in HISTORIC HARLEM…. near the Appollo Theater and The Museum Mile…on Fifth Avenue. Sylvia’s World Famous Resturant…loca | none | Upon arrival please have a legibile copy of your Passport and / or State Photo. ID. as well as your confirmation letter. Please NO SMOKING …LOUD TALKING or PARTIES of any kind. Security Deposit and Cleaning Fees in CASH at time of arrival. Security deposit will be refunded within 72hrs pending no damages. .Cleaning fees are non-refundable. At Check Out… Please dispose of all trash/garbage in the bins located outside behind the entrance stairs. Please place all dirty linens and towels in the dirty laundry bin. Please don’t leave any dishes in the sink and dispose of all Plastic Dishes/Plastic Culinary. If you need a late check-out please contact the Emergency Contact Telephone Numbers that are listed in the Flat. PLEASE LEAVE ALL KEYS IN THE FLAT and BE CERTAIN THE DOORS ARE LOCKED. WE HOPE YOU ENJOYED YOUR STAY and will write positve comments and will visit again. | NA | NA | https://a0.muscache.com/im/pictures/838341/9b3c66f3_original.jpg?aki_policy=large | NA | 4632 | https://www.airbnb.com/users/show/4632 | Elisabeth | 2008-11-25 | New York, New York, United States | Make Up Artist National/ (Website hidden by Airbnb) Production. I m curently working with a Production Company in WDC and Coordinated a “Day Of Service” for the Presidential Innaugration 2013. I can’t live without Starbucks and Oprah’s Chai Tea Latte…and my circle of of great friends world wide. “BLESS ME INTO USEFULLNESS” is my daily prayer. I | within a day | 100% | N/A | f | https://a0.muscache.com/im/users/4632/profile_pic/1328402497/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/4632/profile_pic/1328402497/original.jpg?aki_policy=profile_x_medium | Harlem | 1 | 1 | [‘email’, ‘phone’, ‘google’, ‘reviews’, ‘jumio’, ‘government_id’] | t | t | New York, NY, United States | Harlem | Harlem | Manhattan | New York | NY | 10027 | New York | New York, NY | US | United States | 40.80902 | -73.94190 | t | Apartment | Private room | 2 | 1 | 1 | 1 | Pull-out Sofa | {“Cable TV”,Internet,Wifi,“Air conditioning”,Kitchen,“Buzzer/wireless intercom”,Heating,“Smoke detector”,“Carbon monoxide detector”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”} | NA | $150.00 | $200.00 | $75.00 | 2 | $20.00 | 3 | 7 | 3 | 3 | 7 | 7 | 3 | 7 | 34 months ago | t | 30 | 60 | 90 | 365 | 2019-07-08 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | f | f | f | strict_14_with_grace_period | t | t | 1 | 0 | 1 | 0 | NA | ||||||||||||
| 3831 | https://www.airbnb.com/rooms/3831 | 2.019071e+13 | 2019-07-09 | Cozy Entire Floor of Brownstone | Urban retreat: enjoy 500 s.f. floor in 1899 brownstone, with wood and ceramic flooring throughout (completed Aug. 2015 through Sept. 2015), roomy bdrm, & upgraded kitchen & bathroom (completed Oct. 2015). It’s sunny and loaded with everything you need! | Greetings! We own a double-duplex brownstone in Clinton Hill on Gates near Classon Avenue - (7 blocks to C train, 5 blocks to G train, minutes to all), in which we host on the entire top floor of the upper duplex. This is more of an efficiency set-up: it is the top floor on a two-family, double duplex brownstone. The top floor for our guests consists of a sizable bedroom, full bath and eat-in kitchen for your exclusive use. Our family occupies the floors below. You go through a common hallway and staircase, to get to the top floor (2 easy flights up from the main entrance), but not through any rooms, so it is a fairly private set-up. - Clinton Hill, Gates Avenue near Classon Ave. (1 mi. or less to Williamsburg, Park Slope, Prospect Heights, downtown, Ft. Greene, Bed-Stuy, Bushwick; 20 mins to Manhattan) - includes FiOS, heat (or A/C), hot water, and electricity all included - furnished with two twin beds (convertible into a king bed), one rollaway twin bed and one inflatable | Urban retreat: enjoy 500 s.f. floor in 1899 brownstone, with wood and ceramic flooring throughout (completed Aug. 2015 through Sept. 2015), roomy bdrm, & upgraded kitchen & bathroom (completed Oct. 2015). It’s sunny and loaded with everything you need! Greetings! We own a double-duplex brownstone in Clinton Hill on Gates near Classon Avenue - (7 blocks to C train, 5 blocks to G train, minutes to all), in which we host on the entire top floor of the upper duplex. This is more of an efficiency set-up: it is the top floor on a two-family, double duplex brownstone. The top floor for our guests consists of a sizable bedroom, full bath and eat-in kitchen for your exclusive use. Our family occupies the floors below. You go through a common hallway and staircase, to get to the top floor (2 easy flights up from the main entrance), but not through any rooms, so it is a fairly private set-up. - Clinton Hill, Gates Avenue near Classon Ave. (1 mi. or less to Williamsburg, Park Slope, Pro | none | Just the right mix of urban center and local neighborhood; close to all but enough quiet for a calming walk. | B52 bus for a 10-minute ride to downtown Brooklyn is a few yards away on the corner; G train/Classon Avenue is 5 blocks away; C train is about 6 blocks to either the Clinton/Washington stop or Franklin Avenue stop. There is on-street parking, alternate side is twice per week on the immediate block but only once per week on Classon. From LaGuardia Airport, a taxi will cost $30-$35, but there is also a bus that will put you at the Jackson Heights subway station, and from there it’s about 5 stops to catch the G train, which stops 5 blocks away. From JFK, the taxi is closer to $40, but the AirTran can get you conveniently to the A/C line and the C train is about 6 blocks from here. From JFK via subway/metro/train: From JFK take the AirTrain to Howard Beach to catch the A train toward Brooklyn/Manhattan. Take the A train to Utica Avenue and go across that same platform to catch the C local train (you could also transfer at Nostrand but you would have to carry luggage downstairs to cat | You will have exclusive use of and access to: a sizable private room as described in “The Space” section, furnished with two twin beds (which we will combine into one king bed upon request) and optional rollaway twin and/or inflatable beds, and other small furnishings; full private bath and private eat-in kitchen both renovated in Fall 2015; sizable dining table in sun-filled kitchen area doubles as a great desk space; alcove perfect for vertical bike storage. Upon request you may also have some use of the livingroom on the floor just below. | We’ll be around, but since you have the top floor to yourself, most of the interaction is on the way in or out - we’re open to socializing and did so frequently with our last long-term guests, so it’s really up to you | Smoking - outside please; pets allowed but please contact me first for arrangements | NA | NA | https://a0.muscache.com/im/pictures/e49999c2-9fd5-4ad5-b7cc-224deac989aa.jpg?aki_policy=large | NA | 4869 | https://www.airbnb.com/users/show/4869 | LisaRoxanne | 2008-12-07 | New York, New York, United States | Laid-back bi-coastal actor/professor/attorney. | within a few hours | 93% | N/A | f | https://a0.muscache.com/im/users/4869/profile_pic/1371927771/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/4869/profile_pic/1371927771/original.jpg?aki_policy=profile_x_medium | Clinton Hill | 1 | 1 | [‘email’, ‘phone’, ‘reviews’, ‘kba’] | t | t | Brooklyn, NY, United States | Brooklyn | Clinton Hill | Brooklyn | Brooklyn | NY | 11238 | New York | Brooklyn, NY | US | United States | 40.68514 | -73.95976 | t | Guest suite | Entire home/apt | 3 | 1 | 1 | 4 | Real Bed | {TV,“Cable TV”,Internet,Wifi,“Air conditioning”,Kitchen,“Pets allowed”,“Free street parking”,Heating,“Family/kid friendly”,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,“Lock on bedroom door”,“24-hour check-in”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“Self check-in”,Lockbox,Bathtub,“High chair”,“Stair gates”,“Childrenâs books and toys”,“Pack ân Play/travel crib”,“Hot water”,“Luggage dropoff allowed”,“Long term stays allowed”} | 500 | $89.00 | $575.00 | $2,100.00 | $500.00 | 1 | $0.00 | 1 | 730 | 1 | 1 | 730 | 730 | 1 | 730 | today | t | 0 | 0 | 3 | 194 | 2019-07-09 | 270 | 69 | 2014-09-30 | 2019-07-05 | 90 | 10 | 9 | 10 | 10 | 10 | 9 | f | f | f | moderate | f | f | 1 | 1 | 0 | 0 | 4.64 | ||||
| 5022 | https://www.airbnb.com/rooms/5022 | 2.019071e+13 | 2019-07-08 | Entire Apt: Spacious Studio/Loft by central park | Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem -Â 1 block away from 6 train and 3 blocks from 2 & 3 line. This is in a recently renovated building which includes elevator, trash shoot. marble entrance and laundromat in the basement. The apartment is a spacious loft studio. The seating area and sleeping area is divided by a bookcase. There is a long hallway entrance where the bathroom and closet for your clothes is situated. The apartment is in mint condition, the walls have been freshly painted a few months ago. Supermarket, and 24 hour convenience store less than 1 block away. 1 block away from Hot Yoga Studio and NY Sports club facility. Perfect for anyone wanting to stay in Manhattan but get more space. 10 minutes away from midtown and 15 minutes away from downtown. The neighborhood is lively and diverse. You will need to travel at least 10 blocks to find cafe’s, restaurants etc.. There are a few restaurants on 100 street on | Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem -Â 1 block away from 6 train and 3 blocks from 2 & 3 line. This is in a recently renovated building which includes elevator, trash shoot. marble entrance and laundromat in the basement. The apartment is a spacious loft studio. The seating area and sleeping area is divided by a bookcase. There is a long hallway entrance where the bathroom and closet for your clothes is situated. The apartment is in mint condition, the walls have been freshly painted a few months ago. Supermarket, and 24 hour convenience store less than 1 block away. 1 block away from Hot Yoga Studio and NY Sports club facility. Perfect for anyone wanting to stay in Manhattan but get more space. 10 minutes away from midtown and 15 minutes away from downtown. The neighborhood is lively and diverse. You will need to travel at least 10 blocks to find cafe’s, restaurants etc.. There are a few restaurants on 100 street on | none | Please be considerate when staying in the apartment. This is a low key building and it’s important guest are respectful. You can come and go as you please I just ask that you keep a low profile. 1) Please be respectful of neighbors - no loud music after 10pm and keep a low profile 2) Do not open the door for anyone 3) Please keep the apt clean 4) No access to mailbox - please forward personal mail to job or school | NA | NA | https://a0.muscache.com/im/pictures/feb453bd-fdec-405c-8bfa-3f6963d827e9.jpg?aki_policy=large | NA | 7192 | https://www.airbnb.com/users/show/7192 | Laura | 2009-01-29 | Miami, Florida, United States | I have been a NYer for almost 10 years. I came to NY to study and never left. I work in the advertising industry and love to eat peanut butter & jelly sandwiches. | N/A | N/A | N/A | f | https://a0.muscache.com/im/users/7192/profile_pic/1325651676/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/7192/profile_pic/1325651676/original.jpg?aki_policy=profile_x_medium | East Harlem | 1 | 1 | [‘email’, ‘phone’, ‘facebook’, ‘reviews’, ‘kba’] | t | t | New York, NY, United States | East Harlem | East Harlem | Manhattan | New York | NY | 10029 | New York | New York, NY | US | United States | 40.79851 | -73.94399 | t | Apartment | Entire home/apt | 1 | 1 | NA | 1 | Real Bed | {Internet,Wifi,“Air conditioning”,Kitchen,Elevator,“Free street parking”,“Buzzer/wireless intercom”,Heating,Washer,Dryer,“Smoke detector”,“Carbon monoxide detector”,Essentials,Shampoo,“Hair dryer”,“Hot water”,“Host greets you”} | NA | $80.00 | $600.00 | $1,600.00 | $100.00 | $80.00 | 1 | $20.00 | 10 | 120 | 10 | 10 | 120 | 120 | 10 | 120 | 3 months ago | t | 0 | 0 | 0 | 0 | 2019-07-08 | 9 | 4 | 2012-03-20 | 2018-11-19 | 93 | 10 | 9 | 10 | 10 | 9 | 10 | f | f | f | strict_14_with_grace_period | t | t | 1 | 1 | 0 | 0 | 0.10 | ||||||||
| 5099 | https://www.airbnb.com/rooms/5099 | 2.019071e+13 | 2019-07-08 | Large Cozy 1 BR Apartment In Midtown East | My large 1 bedroom apartment is true New York City living. The apt is in midtown on the east side and centrally located, just a 10-minute walk from Grand Central Station, Empire State Building, Times Square. The kitchen and living room are large and bright with Apple TV. I have a new Queen Bed that sleeps 2 people, and a Queen Aero Bed that can sleep 2 people in the living room. The apartment is located on the 5th floor of a walk up - no elevator (lift). | I have a large 1 bedroom apartment centrally located in Midtown East. A 10 minute walk from Grand Central Station, Times Square, Empire State Building and all major subway and bus lines. The apartment is located on the 5th floor of a pre-war walk up building-no elevator/lift. The apartment is bright with has high ceilings and flow through rooms. A spacious, cozy living room with Netflix and Apple TV. A large bright kitchen to sit and enjoy coffee or tea. The bedroom is spacious with a comfortable queen size bed that sleeps 2. I have a comfortable queen size aero bed that fits in the living room and sleeps 2. It can be tucked away for living space and opened when ready for bed. I’d be happy to give you tips and advice on the best ways to experience the most of NYC. The apartment’s location is great for sightseeing. ** Check out my listing guidebook ** If you would like to stay local in the area, there is a very long & famous strip of bars and restaurants along 3rd Avenue, which | My large 1 bedroom apartment is true New York City living. The apt is in midtown on the east side and centrally located, just a 10-minute walk from Grand Central Station, Empire State Building, Times Square. The kitchen and living room are large and bright with Apple TV. I have a new Queen Bed that sleeps 2 people, and a Queen Aero Bed that can sleep 2 people in the living room. The apartment is located on the 5th floor of a walk up - no elevator (lift). I have a large 1 bedroom apartment centrally located in Midtown East. A 10 minute walk from Grand Central Station, Times Square, Empire State Building and all major subway and bus lines. The apartment is located on the 5th floor of a pre-war walk up building-no elevator/lift. The apartment is bright with has high ceilings and flow through rooms. A spacious, cozy living room with Netflix and Apple TV. A large bright kitchen to sit and enjoy coffee or tea. The bedroom is spacious with a comfortable queen size bed that sleeps 2. I | none | My neighborhood in Midtown East is called Murray Hill. The area is very centrally located with easy access to explore . The apartment is about 5 blocks (7 minute walk) to the United Nations and Grand Central Station the main and most historic train station. Grand Central will give you access to every train in the city. The apartment is also very close to main attractions, It’s about a 10 minute walk to both the Empire State Building and Times Square. There’s a great shopping area with dozens of stores including H&M, Zara, The Gap, BeBe and the world famous Macy’s department store. These shops are a 10 minute walk up East 34th Street from 5th avenue and 8th avenue. If you would like to stay local in the area, there is a very long & famous strip of bars and restaurants along 3rd avenue, which is just around the corner from the apartment. It’s commonly known as the 3rd avenue strip. | Read My Full Listing For All Information. New York City really is the city that doesn’t sleep. There’s a constant flow of people, bikes and cars. The city can be noisy at times, if you’re a light sleeper, ear plugs would help. Check out my local guide book for things to do. | From the apartment is a 10 minute walk to Grand Central Station on East 42nd Street, a 10 minute walk to the Empire State Building on East 34th Street and 5th Avenue, a 10 minute walk to Times Square on West 42 Street and about 20 minutes walk to Central Park on 59th Street. Depending on how long you are staying, I would recommend a 7 day unlimited metro card. This allows you to travel unlimited all day and night on any train or bus in and outside of the city. Grand Central Station is the main NYC train station. You can find any train connection and get anywhere in Manhattan from Grand Central Station. You can get to Brooklyn, Queens, The Bronx and Staten Island from Grand Central Station The M15 bus is around the corner on 2nd avenue. This bus will take you from uptown Harlem to the East Village and South Street Seaport. It will also take you to the Staten Island ferry and Statue of Liberty and everywhere in between along the east side. The M101 bus is just up the street on 3rd aven | I will meet you upon arrival. | I usually check in with guests via text or email. I’m available by text, email or phone call with any questions, suggestions or to help out. | ⢠Check-in time is 2PM. ⢠Check-out time is 12 PM. Please be respectful of the space and leave the apartment in the condition you were welcomed into. | NA | NA | https://a0.muscache.com/im/pictures/0790b1a5-8981-41cc-a370-fa2b982a8803.jpg?aki_policy=large | NA | 7322 | https://www.airbnb.com/users/show/7322 | Chris | 2009-02-02 | New York, New York, United States | I’m an artist, writer, traveler, and a native new yorker. Welcome to my city. | within an hour | 100% | N/A | f | https://a0.muscache.com/im/pictures/user/26745d24-d818-4bf5-8f9e-26b097121ba7.jpg?aki_policy=profile_small | https://a0.muscache.com/im/pictures/user/26745d24-d818-4bf5-8f9e-26b097121ba7.jpg?aki_policy=profile_x_medium | Flatiron District | 1 | 1 | [‘email’, ‘phone’, ‘reviews’, ‘jumio’, ‘government_id’] | t | f | New York, NY, United States | Midtown East | Murray Hill | Manhattan | New York | NY | 10016 | New York | New York, NY | US | United States | 40.74767 | -73.97500 | f | Apartment | Entire home/apt | 2 | 1 | 1 | 1 | Real Bed | {TV,“Cable TV”,Internet,Wifi,Kitchen,“Buzzer/wireless intercom”,Heating,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”,“Hot water”,“Bed linens”,“Extra pillows and blankets”,“Host greets you”} | NA | $200.00 | $300.00 | $125.00 | 2 | $100.00 | 3 | 21 | 3 | 3 | 21 | 21 | 3 | 21 | 3 weeks ago | t | 23 | 48 | 48 | 129 | 2019-07-08 | 74 | 9 | 2009-04-20 | 2019-06-22 | 89 | 10 | 9 | 10 | 10 | 9 | 9 | f | f | f | strict_14_with_grace_period | t | t | 1 | 1 | 0 | 0 | 0.59 |
Data Quality Check
Missing Values Majority of description columns and host related information are blank. Steps are taken in the following section to filter columns with higher percentage of Nulls/NA.
It is also important how we handle missing value for example based on the variable and number of missing values we can either remove the missing values , or depending on the requirement we can impute the missing values by 0 or mean , median , mode , regression etc.
numMissingVal <-sapply(revenue, function(x) sum(length(which(is.na(x)))))
kable(as.data.frame(numMissingVal)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| numMissingVal | |
|---|---|
| id | 0 |
| listing_url | 0 |
| scrape_id | 0 |
| last_scraped | 0 |
| name | 0 |
| summary | 0 |
| space | 0 |
| description | 0 |
| experiences_offered | 0 |
| neighborhood_overview | 0 |
| notes | 0 |
| transit | 0 |
| access | 0 |
| interaction | 0 |
| house_rules | 0 |
| thumbnail_url | 48895 |
| medium_url | 48895 |
| picture_url | 0 |
| xl_picture_url | 48895 |
| host_id | 0 |
| host_url | 0 |
| host_name | 0 |
| host_since | 0 |
| host_location | 0 |
| host_about | 0 |
| host_response_time | 0 |
| host_response_rate | 0 |
| host_acceptance_rate | 0 |
| host_is_superhost | 0 |
| host_thumbnail_url | 0 |
| host_picture_url | 0 |
| host_neighbourhood | 0 |
| host_listings_count | 21 |
| host_total_listings_count | 21 |
| host_verifications | 0 |
| host_has_profile_pic | 0 |
| host_identity_verified | 0 |
| street | 0 |
| neighbourhood | 0 |
| neighbourhood_cleansed | 0 |
| neighbourhood_group_cleansed | 0 |
| city | 0 |
| state | 0 |
| zipcode | 0 |
| market | 0 |
| smart_location | 0 |
| country_code | 0 |
| country | 0 |
| latitude | 0 |
| longitude | 0 |
| is_location_exact | 0 |
| property_type | 0 |
| room_type | 0 |
| accommodates | 0 |
| bathrooms | 56 |
| bedrooms | 22 |
| beds | 40 |
| bed_type | 0 |
| amenities | 0 |
| square_feet | 48487 |
| price | 0 |
| weekly_price | 0 |
| monthly_price | 0 |
| security_deposit | 0 |
| cleaning_fee | 0 |
| guests_included | 0 |
| extra_people | 0 |
| minimum_nights | 0 |
| maximum_nights | 0 |
| minimum_minimum_nights | 0 |
| maximum_minimum_nights | 0 |
| minimum_maximum_nights | 0 |
| maximum_maximum_nights | 0 |
| minimum_nights_avg_ntm | 0 |
| maximum_nights_avg_ntm | 0 |
| calendar_updated | 0 |
| has_availability | 0 |
| availability_30 | 0 |
| availability_60 | 0 |
| availability_90 | 0 |
| availability_365 | 0 |
| calendar_last_scraped | 0 |
| number_of_reviews | 0 |
| number_of_reviews_ltm | 0 |
| first_review | 0 |
| last_review | 0 |
| review_scores_rating | 11022 |
| review_scores_accuracy | 11060 |
| review_scores_cleanliness | 11043 |
| review_scores_checkin | 11078 |
| review_scores_communication | 11055 |
| review_scores_location | 11082 |
| review_scores_value | 11080 |
| requires_license | 0 |
| license | 0 |
| jurisdiction_names | 0 |
| instant_bookable | 0 |
| is_business_travel_ready | 0 |
| cancellation_policy | 0 |
| require_guest_profile_picture | 0 |
| require_guest_phone_verification | 0 |
| calculated_host_listings_count | 0 |
| calculated_host_listings_count_entire_homes | 0 |
| calculated_host_listings_count_private_rooms | 0 |
| calculated_host_listings_count_shared_rooms | 0 |
| reviews_per_month | 10052 |
Account for Missing Zipcodes Zipcode column contains 611 missing values. Ignoring these values can prove detrimental to the analysis. Zipcodes are imputed by selecting a non-NA value from Neighbourhood Group Cleansed.
Total Number of NA values in Zipcode Column after Imputation:
revenue <-
revenue %>% group_by(neighbourhood_group_cleansed) %>% fill(zipcode) %>% ungroup()
sum(is.array(revenue$zipcode))## [1] 0Dollar-ed Price Columns Value prefix of every price row prevents numeric manipulation. It is thus removed from three columns: Price, Weekly Price & Monthly Price.
revenue$price <- as.numeric(gsub('[$,]','', revenue$price))
revenue$weekly_price <- as.numeric(gsub('[$,]','', revenue$weekly_price))
revenue$monthly_price <- as.numeric(gsub('[$,]','', revenue$monthly_price))
head(revenue[c("price","weekly_price","monthly_price")])Negative or Zero Valued Columns None of the price columns: Price, weekly price & Monthly price have zero or negative value.
revCharCol <- colnames(revenue %>% ungroup() %>% select_if(is.character))
revZeroNeg <-
sapply(revenue[,!(names(revenue) %in% revCharCol)], function(x)
count(x <= 0, na.rm = TRUE))## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
## Warning in Ops.factor(x, 0): '<=' not meaningful for factorskable(as.data.frame(revZeroNeg)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| revZeroNeg | |
|---|---|
| id | 0 |
| listing_url | 0 |
| scrape_id | 0 |
| last_scraped | 0 |
| name | 0 |
| summary | 0 |
| space | 0 |
| description | 0 |
| experiences_offered | 0 |
| neighborhood_overview | 0 |
| notes | 0 |
| transit | 0 |
| access | 0 |
| interaction | 0 |
| house_rules | 0 |
| thumbnail_url | 0 |
| medium_url | 0 |
| picture_url | 0 |
| xl_picture_url | 0 |
| host_id | 0 |
| host_url | 0 |
| host_name | 0 |
| host_since | 0 |
| host_location | 0 |
| host_about | 0 |
| host_response_time | 0 |
| host_response_rate | 0 |
| host_acceptance_rate | 0 |
| host_is_superhost | 0 |
| host_thumbnail_url | 0 |
| host_picture_url | 0 |
| host_neighbourhood | 0 |
| host_listings_count | 901 |
| host_total_listings_count | 901 |
| host_verifications | 0 |
| host_has_profile_pic | 0 |
| host_identity_verified | 0 |
| street | 0 |
| neighbourhood | 0 |
| neighbourhood_cleansed | 0 |
| neighbourhood_group_cleansed | 0 |
| city | 0 |
| state | 0 |
| zipcode | 0 |
| market | 0 |
| smart_location | 0 |
| country_code | 0 |
| country | 0 |
| latitude | 0 |
| longitude | 48895 |
| is_location_exact | 0 |
| property_type | 0 |
| room_type | 0 |
| accommodates | 0 |
| bathrooms | 126 |
| bedrooms | 4569 |
| beds | 1094 |
| bed_type | 0 |
| amenities | 0 |
| square_feet | 34 |
| price | 11 |
| weekly_price | 0 |
| monthly_price | 0 |
| security_deposit | 0 |
| cleaning_fee | 0 |
| guests_included | 0 |
| extra_people | 0 |
| minimum_nights | 0 |
| maximum_nights | 0 |
| minimum_minimum_nights | 0 |
| maximum_minimum_nights | 0 |
| minimum_maximum_nights | 0 |
| maximum_maximum_nights | 0 |
| minimum_nights_avg_ntm | 0 |
| maximum_nights_avg_ntm | 0 |
| calendar_updated | 0 |
| has_availability | 0 |
| availability_30 | 24471 |
| availability_60 | 20503 |
| availability_90 | 19357 |
| availability_365 | 17533 |
| calendar_last_scraped | 0 |
| number_of_reviews | 10052 |
| number_of_reviews_ltm | 19747 |
| first_review | 0 |
| last_review | 0 |
| review_scores_rating | 0 |
| review_scores_accuracy | 0 |
| review_scores_cleanliness | 0 |
| review_scores_checkin | 0 |
| review_scores_communication | 0 |
| review_scores_location | 0 |
| review_scores_value | 0 |
| requires_license | 0 |
| license | 0 |
| jurisdiction_names | 0 |
| instant_bookable | 0 |
| is_business_travel_ready | 0 |
| cancellation_policy | 0 |
| require_guest_profile_picture | 0 |
| require_guest_phone_verification | 0 |
| calculated_host_listings_count | 0 |
| calculated_host_listings_count_entire_homes | 21101 |
| calculated_host_listings_count_private_rooms | 24149 |
| calculated_host_listings_count_shared_rooms | 47205 |
| reviews_per_month | 0 |
check for row duplication
revenue[which(duplicated(revenue) ==T),] Data Filtering Inorder to remove columns that add little or no value to the analysis, some of the smart data munging techniques are incorporated. These include removing columns based on pattern matching with their names, imbalanced columns, character columns with 100 % variance etc.
Other variable reduction methods are Principal Component Analysis , Variable clustering based on 1-R2 ratio value we can select the most relevant variables in each cluster.
Account for Zero Variance, Imbalanced, high NA valued and 100 percent variance character columns. Use associated methods to remove these columns from revenue data. Worst case - Manually remove columns keeping the final outcome in mind.
Imbalanced/Zero Variance columns add no value to the analysis. These columns are removed using nearzeroVar method from Caret package.
#Columns Removed:
zvdf <- nearZeroVar(revenue, saveMetrics = TRUE)
ZVnames=rownames(subset(zvdf, nzv== TRUE))
revenue <- revenue[ , !(names(revenue) %in% ZVnames)]
#printlis(ZVnames)Pattern Matching Column names starting with ???calendar???,???require???, ???host??? and ending with ???url??? and ???nights??? are irrelevant information when the property is invested in, by the real estate company. These columns are removed.
These methods are taken after going through the overall data and based on what we are targeting as our target variable.
Columns Removed:
pattern <-
colnames(
revenue %>% select(
starts_with("require"),
starts_with("host"),
starts_with("calendar"),
ends_with("url"),
ends_with("nights")
)
)
revenue <- revenue[,!(names(revenue) %in% pattern)]Based on Unique Values - Character Columns Character columns with near 100% variance are removed as they provide no group level information that can be used on a larger population scale.
These columns include textual columns describing the home, host, amenties etc. For lack of conclusion from other variables, these columns can be used for sentimenta analysis.
I have done sentimental analysis using python libraries wherein we have done the following: -We have selected the ID and the summary columns of the Airbnb data for our sentimental analysis. -The summary gives us an idea of the place where the customers would live and to make it appealing for the customers the owners must portray the details in the most effective way possible. -Our sentimental analysis tells us whether the summary provided by the owner for a room/apartment appeals the customers in a positive or a negative way. -The wordcloud shows us the intensity of the words that tells us about the word frequency. So, in this case we have made wordclouds for the positive as well as the negative summary. -This would help the owners to change their way towards the marketing side, so that they can use the most used positive words in order to increase their customer staying at their place resulting in more revenue for themselves
Columns Removed:
We will also drop columns which have more than 50% missing values
nadf <- revenue %>% summarise_all(funs(sum(is.na(.))))## Warning: funs() is soft deprecated as of dplyr 0.8.0
## please use list() instead
##
## # Before:
## funs(name = f(.)
##
## # After:
## list(name = ~f(.))
## This warning is displayed once per session.nadf <- nadf %>% gather(key = var_name, value = value, 1:ncol(nadf))
nadf$numNa <- round(nadf$value/nrow(revenue),2)
naval <- nadf %>% filter(numNa > 0.5) %>% pull(var_name)
revenue <- revenue[,!(names(revenue) %in% naval)]
#naval
#revenue
dropcol <- c('id',
'street',
'city',
'CountyName',
'cleaning_fee',
'guests_included',
'extra_people',
'review_scores_communication',
'review_scores_checkin',
'review_scores_cleanliness',
'review_scores_value',
'reviews_per_month',
'instant_bookable',
'cancellation_policy',
'smart_location',
'is_location_exact',
'calculated_host_listings_count',
'name',
'summary',
'space',
'description',
'neighborhood_overview',
'notes',
'transit',
'access',
'interaction',
'house_rules',
'amenities'
)
head(dropcol)## [1] "id" "street" "city" "CountyName"
## [5] "cleaning_fee" "guests_included"dropping all the unwanted columns based on various statistical significance, analyzing PCA and the variance lost if we removed these columns
revenue <- revenue[,!(names(revenue) %in% dropcol)]
dim(revenue) ## [1] 48895 29overall the number of variables have reduced to 29 now
Clean Revenue Data
kable(head(revenue)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| last_scraped | neighbourhood | neighbourhood_cleansed | neighbourhood_group_cleansed | zipcode | latitude | longitude | property_type | room_type | accommodates | bathrooms | bedrooms | beds | price | security_deposit | minimum_nights_avg_ntm | maximum_nights_avg_ntm | availability_30 | availability_60 | availability_90 | availability_365 | number_of_reviews | number_of_reviews_ltm | last_review | review_scores_rating | review_scores_accuracy | review_scores_location | calculated_host_listings_count_entire_homes | calculated_host_listings_count_private_rooms |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019-07-08 | Highbridge | Highbridge | Bronx | 10452 | 40.83232 | -73.93184 | House | Private room | 1 | 1.0 | 1 | 1 | 40 | $0.00 | 1 | 2 | 22 | 50 | 78 | 353 | 219 | 86 | 2019-07-04 | 98 | 10 | 9 | 0 | 3 |
| 2019-07-08 | Highbridge | Highbridge | Bronx | 10452 | 40.83075 | -73.93058 | House | Private room | 2 | 1.5 | 1 | 1 | 45 | $0.00 | 1 | 28 | 21 | 37 | 56 | 323 | 138 | 59 | 2019-06-30 | 97 | 10 | 10 | 0 | 3 |
| 2019-07-09 | The Bronx | Clason Point | Bronx | 10473 | 40.81309 | -73.85514 | House | Private room | 3 | 1.0 | 1 | 2 | 90 | $0.00 | 2 | 1125 | 25 | 55 | 82 | 349 | 0 | 0 | NA | NA | NA | 2 | 4 | |
| 2019-07-09 | Eastchester | Eastchester | Bronx | 10475 | 40.88057 | -73.83572 | Apartment | Entire home/apt | 2 | 1.0 | 1 | 1 | 105 | $150.00 | 2 | 1125 | 30 | 60 | 90 | 365 | 38 | 14 | 2019-06-27 | 98 | 10 | 9 | 6 | 6 |
| 2019-07-08 | Kingsbridge Heights | Kingsbridge | Bronx | 10468 | 40.87207 | -73.90193 | Apartment | Entire home/apt | 2 | 1.0 | 1 | 1 | 90 | $0.00 | 30 | 330 | 11 | 41 | 71 | 346 | 4 | 4 | 2019-01-02 | 90 | 10 | 10 | 1 | 1 |
| 2019-07-09 | The Bronx | Woodlawn | Bronx | 10470 | 40.89747 | -73.86390 | House | Entire home/apt | 4 | 1.0 | 1 | 3 | 77 | $100.00 | 1 | 9 | 13 | 24 | 47 | 309 | 197 | 51 | 2019-06-23 | 92 | 9 | 9 | 1 | 0 |
Joining both the clean data based on ZipCOdes and RegionName Revenue and Cost Data are merged based on common regionName/Zipcode. The real estate company has already chosen 2 Bed room homes as profitable venture.
Data is further refined to account for only 2 bed room homes. Dimension of the final data set:
final <- merge(revenue, cost, by.x = "zipcode",by.y = "RegionName")
final <- subset(final,final$bedrooms == '2')
dim(final)## [1] 1565 33Exploratory Data Analysis understanding data
kable(head(final)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| zipcode | last_scraped | neighbourhood | neighbourhood_cleansed | neighbourhood_group_cleansed | latitude | longitude | property_type | room_type | accommodates | bathrooms | bedrooms | beds | price | security_deposit | minimum_nights_avg_ntm | maximum_nights_avg_ntm | availability_30 | availability_60 | availability_90 | availability_365 | number_of_reviews | number_of_reviews_ltm | last_review | review_scores_rating | review_scores_accuracy | review_scores_location | calculated_host_listings_count_entire_homes | calculated_host_listings_count_private_rooms | CountyName | SizeRank | cost | year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 10003 | 2019-07-08 | Manhattan | East Village | Manhattan | 40.73245 | -73.98535 | Apartment | Entire home/apt | 5 | 1 | 2 | 4 | 300 | $500.00 | 30.0 | 100 | 30 | 60 | 90 | 180 | 0 | 0 | NA | NA | NA | 1 | 0 | New York | 21 | 2005500 | 2017 | |
| 4 | 10003 | 2019-07-08 | Manhattan | Gramercy | Manhattan | 40.73598 | -73.98161 | Apartment | Entire home/apt | 4 | 1 | 2 | 3 | 125 | 1.0 | 1125 | 0 | 0 | 0 | 0 | 19 | 0 | 2016-06-23 | 88 | 9 | 10 | 1 | 0 | New York | 21 | 2005500 | 2017 | |
| 6 | 10003 | 2019-07-08 | East Village | East Village | Manhattan | 40.72862 | -73.98885 | Apartment | Entire home/apt | 5 | 1 | 2 | 2 | 142 | $150.00 | 1.0 | 1125 | 1 | 10 | 12 | 242 | 214 | 46 | 2019-07-05 | 92 | 9 | 10 | 1 | 0 | New York | 21 | 2005500 | 2017 |
| 24 | 10003 | 2019-07-08 | Gramercy Park | Gramercy | Manhattan | 40.73268 | -73.98399 | Apartment | Entire home/apt | 5 | 1 | 2 | 3 | 199 | $300.00 | 1.6 | 1125 | 11 | 39 | 57 | 72 | 26 | 26 | 2019-06-30 | 96 | 10 | 10 | 1 | 0 | New York | 21 | 2005500 | 2017 |
| 31 | 10003 | 2019-07-08 | Manhattan | East Village | Manhattan | 40.73029 | -73.98836 | Apartment | Entire home/apt | 3 | 1 | 2 | 2 | 62 | $0.00 | 4.0 | 1125 | 0 | 0 | 5 | 5 | 4 | 1 | 2018-07-08 | 100 | 10 | 10 | 1 | 0 | New York | 21 | 2005500 | 2017 |
| 32 | 10003 | 2019-07-08 | Manhattan | East Village | Manhattan | 40.73167 | -73.98581 | Apartment | Entire home/apt | 3 | 1 | 2 | 3 | 140 | 7.0 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | 1 | 0 | New York | 21 | 2005500 | 2017 |
COnverting char to factor Data Type Handling Convert ???Character??? columns to Factors to ease the process of building discrete charts.
final <- final %>% mutate_if(sapply(final,is.character),as.factor)
str(final)## 'data.frame': 1565 obs. of 33 variables:
## $ zipcode : Factor w/ 200 levels "","07093","07302",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ last_scraped : Factor w/ 2 levels "2019-07-08","2019-07-09": 1 1 1 1 1 1 1 1 1 1 ...
## $ neighbourhood : Factor w/ 195 levels "","Allerton",..: 106 106 55 72 106 106 55 106 106 106 ...
## $ neighbourhood_cleansed : Factor w/ 221 levels "Allerton","Arden Heights",..: 65 87 65 87 65 65 65 87 87 93 ...
## $ neighbourhood_group_cleansed : Factor w/ 5 levels "Bronx","Brooklyn",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ latitude : num 40.7 40.7 40.7 40.7 40.7 ...
## $ longitude : num -74 -74 -74 -74 -74 ...
## $ property_type : Factor w/ 36 levels "Aparthotel","Apartment",..: 2 2 2 2 2 2 28 2 2 2 ...
## $ room_type : Factor w/ 3 levels "Entire home/apt",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accommodates : int 5 4 5 5 3 3 6 4 4 4 ...
## $ bathrooms : num 1 1 1 1 1 1 2 1 1 2.5 ...
## $ bedrooms : int 2 2 2 2 2 2 2 2 2 2 ...
## $ beds : int 4 3 2 3 2 3 4 2 2 2 ...
## $ price : num 300 125 142 199 62 140 260 239 280 999 ...
## $ security_deposit : Factor w/ 225 levels "","$0.00","$1,000.00",..: 178 1 56 137 2 1 1 2 1 176 ...
## $ minimum_nights_avg_ntm : num 30 1 1 1.6 4 7 3 5 3.3 3 ...
## $ maximum_nights_avg_ntm : num 100 1125 1125 1125 1125 ...
## $ availability_30 : int 30 0 1 11 0 0 2 0 0 23 ...
## $ availability_60 : int 60 0 10 39 0 0 10 17 0 44 ...
## $ availability_90 : int 90 0 12 57 5 0 26 17 0 74 ...
## $ availability_365 : int 180 0 242 72 5 0 26 266 0 164 ...
## $ number_of_reviews : int 0 19 214 26 4 0 24 2 0 11 ...
## $ number_of_reviews_ltm : int 0 0 46 26 1 0 24 0 0 2 ...
## $ last_review : Factor w/ 1765 levels "","2011-03-28",..: 1 666 1762 1757 1400 1 1758 1213 1 1700 ...
## $ review_scores_rating : int NA 88 92 96 100 NA 96 90 NA 100 ...
## $ review_scores_accuracy : int NA 9 9 10 10 NA 10 8 NA 10 ...
## $ review_scores_location : int NA 10 10 10 10 NA 10 10 NA 10 ...
## $ calculated_host_listings_count_entire_homes : int 1 1 1 1 1 1 2 2 1 1 ...
## $ calculated_host_listings_count_private_rooms: int 0 0 0 0 0 0 0 1 0 0 ...
## $ CountyName : Factor w/ 722 levels "Ada","Adams",..: 460 460 460 460 460 460 460 460 460 460 ...
## $ SizeRank : int 21 21 21 21 21 21 21 21 21 21 ...
## $ cost : num 2005500 2005500 2005500 2005500 2005500 ...
## $ year : num 2017 2017 2017 2017 2017 ...Corrected Price by Room Type: Following are the assumptions made: Price of the daily rental in Revenue data is reflective of the space that is offered and not the entire property itself. The price must be specifically corrected to account for entire property to account the benefit.
Assumption Made: If the property type == Private Room, it is multipled by number of bedrooms to account for overall price. Correction applied is returned to original price column.
final <- final %>% mutate(price = if_else(room_type == "Private room",
price * bedrooms,
price))
finalMissing Values Nothing too alarming, major chunk of columns look okay to proceed with the analysis.
gg_miss_upset(final)
number of properties by neighbourhood
ggplot(final, aes(neighbourhood_group_cleansed)) + geom_bar() + labs(x = "Neighbourhood", y = "Number of Properties") + geom_text(stat='count', aes(label=..count..), vjust= -0.3) + theme_classic()
number of properties by zipcode
ggplot(final, aes(zipcode, fill = neighbourhood_group_cleansed)) + geom_bar() + labs(x = "Zipcode", y = "Number of Properties") + geom_text(stat='count', aes(label=..count..), vjust= -0.3) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1))
Property cost by zipcode
medianCost1 <- final %>% select(zipcode,neighbourhood_group_cleansed, cost) %>% filter(neighbourhood_group_cleansed == c("Manhattan","Brooklyn")) %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(median)) %>% ggplot(aes(x = zipcode, y = cost, fill =neighbourhood_group_cleansed )) + geom_bar(stat = "identity") +scale_y_continuous(labels = scales::comma) + labs(y = "Cost", x = "Zipcode") + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))## Warning in `==.default`(neighbourhood_group_cleansed, c("Manhattan",
## "Brooklyn")): longer object length is not a multiple of shorter object
## length## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object lengthmedianCost2 <- final %>% select(zipcode,neighbourhood_group_cleansed, cost) %>% filter(neighbourhood_group_cleansed == c("Staten Island","Queens")) %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(median)) %>% ggplot(aes(x = zipcode, y = cost, fill =neighbourhood_group_cleansed )) + geom_bar(stat = "identity") +scale_y_continuous(labels = scales::comma) + labs(x = "Cost", y = "Zipcode") + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))## Warning in `==.default`(neighbourhood_group_cleansed, c("Staten Island", :
## longer object length is not a multiple of shorter object length
## Warning in `==.default`(neighbourhood_group_cleansed, c("Staten Island", :
## longer object length is not a multiple of shorter object lengthcowplot::plot_grid(medianCost1, medianCost2, labels = "AUTO", ncol = 1, align = 'v')
Property type Vs Cost In order to develop deeper understanding with our data, property cost is measured across different property types: Apartment, House, Loft etc. Based on the analysis we can see the effect a property type has on the cost of it.
propTypeCost <- ggplot(final, aes(x=property_type, y=cost, fill = zipcode)) + scale_y_continuous(labels = scales::comma) + geom_point(aes(col = zipcode, size=cost)) +
geom_smooth(method="loess", se=F) + labs( x="Property type", y="Cost") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplotly(propTypeCost)Types of property rented in nyc
property_df <- final %>%
filter(property_type %in% c("Apartment","Condominium", "House",
"Loft")) %>%
group_by(neighbourhood_group_cleansed) %>%
mutate(total_property = n()) %>%
group_by(neighbourhood_group_cleansed, property_type) %>%
mutate(borough_prop_count = n())
property_df2 <-unique(select(property_df, neighbourhood_group_cleansed,
property_type, total_property, borough_prop_count)) %>%
mutate(ratio = borough_prop_count/total_property)
property_df2 <- bind_rows(property_df2, list(neighbourhood_group_cleansed = "Staten Island",property_type = "Condominium", total_property = 0, borough_prop_count = 0, ratio = 0))## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vectorproperty_df2 <- bind_rows(property_df2, list(neighbourhood_group_cleansed = "Staten Island",property_type = "Loft", total_property = 0, borough_prop_count = 0, ratio = 0))
ggplot(property_df2, aes(x = neighbourhood_group_cleansed, y = ratio,
fill = property_type)) +
geom_bar(position = "dodge",stat="identity") +
xlab("Boroughs") + ylab("Count") +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual("Property Type",values=c("#357b8a","turquoise3", "#ff5a5f",
"darkseagreen")) +
ggtitle("Types of Properties Rented in NYC") +
theme_minimal()+
theme(text = element_text(family = "Georgia", size = 10, face = "bold"),
plot.title = element_text(size = 16,color = "#ff5a5f", margin = margin(b = 7)),
plot.subtitle = element_text(size = 10, color = "darkslategrey", margin = margin(b = 7)))## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Most popular zipcodes
final %>% group_by(zipcode) %>%
summarise(num = n()) %>%
arrange() %>%
top_n(20) %>%
ggplot(aes(x = reorder(zipcode,num), y = num))+
geom_bar(stat = "identity", fill = "#00FF00") +
coord_flip() +
labs(title = "Most popular zipcodes",
subtitle = "zipcodes with most number of Airbnb hosts",
colour = " Borough") +
xlab("Zipcode") +
ylab("Number of Airbnb Rentals") +
theme_minimal()+
theme(text = element_text(size = 10),
plot.title = element_text(size = 16,color = "#00FF00",
face = "bold",margin = margin(b = 7)),
plot.subtitle = element_text(size = 10, color = "darkslategrey",
margin = margin(b = 7)))## Selecting by num
pricebynbghrhood <- ggplot(final,aes(x=price, fill=neighbourhood_group_cleansed)) + geom_density(alpha = 0.3) +
scale_x_continuous(limits = quantile(final$price, c(0, 0.99))) + labs(x = "Price/Night", y = "Density") +
guides(fill = guide_legend(title = "Neighbourhood"))
ggplotly(pricebynbghrhood)## Warning: Removed 16 rows containing non-finite values (stat_density).pricebyZipcode <- ggplot(final, aes(x = zipcode,y = price, fill = neighbourhood_group_cleansed)) + geom_boxplot() + scale_y_continuous(limits = quantile(final$price, c(0, 0.99))) + labs(x = "Zipcode", y = "Price/Night") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))
ggplotly(pricebyZipcode)## Warning: Removed 16 rows containing non-finite values (stat_boxplot).Pair Wise Correlation: Correlation gives us a priliminary idea of how are variables are correlated and which of them could cause multicollinearity
If a property is generously available for booking in the 30 days, it is most likely to be generously available during the rest of the year as well. There is strong positive correlation between (Availablity 30, Availablity 60,Availablity 90 & Availablity 365). The opposite is true. Booked out apartments tend to remain popular across the year.
Interestingly, Number of reviews also drive earlier bookings, which is clearly seen by its positive correlation with column: Availablity 365.
Occupancy - Zipcode Vs Availability Lower the availablity , higher the occupancy - indicating fast-filling properties.
Looking at Availablity for next 365 days, it is surprising to find a lot of properties booked ahead of time. The company can capitalize on this behavior and vary the price accordindingly to generate more revenue/profits.
Zipcodes that make top 10 from this list: 10021,10023,10025,10028,10128,10304,10312,11201,11215,11231
corCols <- c("availability_30","availability_60","availability_90","availability_365","number_of_reviews","price","cost")
plot_correlation(final[corCols])
final %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(mean)) %>%
ggplot(aes(x =zipcode,y= availability_365, fill = neighbourhood_group_cleansed )) + geom_bar(stat = "identity") + scale_y_continuous(labels = scales::comma) + scale_colour_brewer(palette = "Pastel2") + labs( y = "Availability", x = "Zipcode") + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood")) ## Warning in mean.default(last_scraped): argument is not numeric or logical:
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## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
Availability Vs Price Stepping aside to understand availablity in terms of price, lower price dont exactly drive faster bookings which is a very important observation while strategizing in investing on real estate. Higher rental properties have minimal availablity (compared to next 365 days). It is satisfying to know that people are ready to pay higher ahead of time which showcases the purchasing power of a customer.
availablityPrice <- final %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(mean)) %>% ggplot(aes(x=availability_365, y=price)) + scale_colour_brewer(palette = "Set1") + geom_point(aes(col=neighbourhood_group_cleansed, size=price)) +
labs(x="Availability", y="Price", shape="Price", colour= "Neighbourhood")## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NAggplotly(availablityPrice)final$rating <- round(final$review_scores_rating/10,0)
final$rating <- as.factor(final$rating )
reviewPrice <- ggplot(data = subset(final, !is.na(rating)), aes(x=rating, y=price, colour = zipcode)) + geom_point(aes( size=price)) + scale_y_continuous(limits = quantile(final$price, c(0, 0.99))) + labs(x="Rating", y="Price")
ggplotly(reviewPrice)Performing steps in order to fit various models in order to predict price. Following are the steps done to clean the data again and select the columns which are relevant for model selection and evaluation. We can use various models like xgboost and evaluate the models using various Information criteria like AIC , BIC , SBC etc. We also can plot the ROC curve to understand how well the model is.
inp_data <- read.csv("C:/Users/Kartik/Airbnb/airbnbbbbbbb.csv", header = TRUE,stringsAsFactors = F)
dim(inp_data)## [1] 48895 106names(inp_data)## [1] "id"
## [2] "listing_url"
## [3] "scrape_id"
## [4] "last_scraped"
## [5] "name"
## [6] "summary"
## [7] "space"
## [8] "description"
## [9] "experiences_offered"
## [10] "neighborhood_overview"
## [11] "notes"
## [12] "transit"
## [13] "access"
## [14] "interaction"
## [15] "house_rules"
## [16] "thumbnail_url"
## [17] "medium_url"
## [18] "picture_url"
## [19] "xl_picture_url"
## [20] "host_id"
## [21] "host_url"
## [22] "host_name"
## [23] "host_since"
## [24] "host_location"
## [25] "host_about"
## [26] "host_response_time"
## [27] "host_response_rate"
## [28] "host_acceptance_rate"
## [29] "host_is_superhost"
## [30] "host_thumbnail_url"
## [31] "host_picture_url"
## [32] "host_neighbourhood"
## [33] "host_listings_count"
## [34] "host_total_listings_count"
## [35] "host_verifications"
## [36] "host_has_profile_pic"
## [37] "host_identity_verified"
## [38] "street"
## [39] "neighbourhood"
## [40] "neighbourhood_cleansed"
## [41] "neighbourhood_group_cleansed"
## [42] "city"
## [43] "state"
## [44] "zipcode"
## [45] "market"
## [46] "smart_location"
## [47] "country_code"
## [48] "country"
## [49] "latitude"
## [50] "longitude"
## [51] "is_location_exact"
## [52] "property_type"
## [53] "room_type"
## [54] "accommodates"
## [55] "bathrooms"
## [56] "bedrooms"
## [57] "beds"
## [58] "bed_type"
## [59] "amenities"
## [60] "square_feet"
## [61] "price"
## [62] "weekly_price"
## [63] "monthly_price"
## [64] "security_deposit"
## [65] "cleaning_fee"
## [66] "guests_included"
## [67] "extra_people"
## [68] "minimum_nights"
## [69] "maximum_nights"
## [70] "minimum_minimum_nights"
## [71] "maximum_minimum_nights"
## [72] "minimum_maximum_nights"
## [73] "maximum_maximum_nights"
## [74] "minimum_nights_avg_ntm"
## [75] "maximum_nights_avg_ntm"
## [76] "calendar_updated"
## [77] "has_availability"
## [78] "availability_30"
## [79] "availability_60"
## [80] "availability_90"
## [81] "availability_365"
## [82] "calendar_last_scraped"
## [83] "number_of_reviews"
## [84] "number_of_reviews_ltm"
## [85] "first_review"
## [86] "last_review"
## [87] "review_scores_rating"
## [88] "review_scores_accuracy"
## [89] "review_scores_cleanliness"
## [90] "review_scores_checkin"
## [91] "review_scores_communication"
## [92] "review_scores_location"
## [93] "review_scores_value"
## [94] "requires_license"
## [95] "license"
## [96] "jurisdiction_names"
## [97] "instant_bookable"
## [98] "is_business_travel_ready"
## [99] "cancellation_policy"
## [100] "require_guest_profile_picture"
## [101] "require_guest_phone_verification"
## [102] "calculated_host_listings_count"
## [103] "calculated_host_listings_count_entire_homes"
## [104] "calculated_host_listings_count_private_rooms"
## [105] "calculated_host_listings_count_shared_rooms"
## [106] "reviews_per_month"a <- strsplit(inp_data$amenities,",")Removing variables that contain only a single value
data1 <- inp_data
a <- apply(data1,2,unique)
is.vector(a)## [1] TRUElength(a)## [1] 106b <- NULL
for(i in 1:length(a))
{
if(length(a[[i]])==1){
b <- rbind(b,names(a[[i]]))
}
}
b <- as.vector(b)
head(b)## NULLdata1[,b] <- NULL
dim(data1)## [1] 48895 106All the url related variables can be removed as they contain weblinks which aren’t useful for any kind of analysis
data1[,names(data1)[grep("url",names(data1))]] <- NULL
grep("url",names(data1))## integer(0)This leads to removal of 8 such variables.Next we removed all those variables which have more than 80% missing values to get more reformed and clean data
a <- apply(data1,2,is.na)
head(a)## id scrape_id last_scraped name summary space description
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## experiences_offered neighborhood_overview notes transit access
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## interaction house_rules host_id host_name host_since host_location
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE FALSE
## host_about host_response_time host_response_rate host_acceptance_rate
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## host_is_superhost host_neighbourhood host_listings_count
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## host_total_listings_count host_verifications host_has_profile_pic
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## host_identity_verified street neighbourhood neighbourhood_cleansed
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## neighbourhood_group_cleansed city state zipcode market
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## smart_location country_code country latitude longitude
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## is_location_exact property_type room_type accommodates bathrooms
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## bedrooms beds bed_type amenities square_feet price weekly_price
## [1,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5,] TRUE FALSE FALSE FALSE TRUE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## monthly_price security_deposit cleaning_fee guests_included
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## extra_people minimum_nights maximum_nights minimum_minimum_nights
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## maximum_minimum_nights minimum_maximum_nights maximum_maximum_nights
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## minimum_nights_avg_ntm maximum_nights_avg_ntm calendar_updated
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## has_availability availability_30 availability_60 availability_90
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## availability_365 calendar_last_scraped number_of_reviews
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## number_of_reviews_ltm first_review last_review review_scores_rating
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE TRUE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## review_scores_accuracy review_scores_cleanliness
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] TRUE TRUE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## review_scores_checkin review_scores_communication
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] TRUE TRUE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## review_scores_location review_scores_value requires_license license
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
## [3,] TRUE TRUE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE
## jurisdiction_names instant_bookable is_business_travel_ready
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## cancellation_policy require_guest_profile_picture
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## require_guest_phone_verification calculated_host_listings_count
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## calculated_host_listings_count_entire_homes
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE
## calculated_host_listings_count_private_rooms
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE
## calculated_host_listings_count_shared_rooms reviews_per_month
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE TRUE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSEb <- apply(a,2,sum)
is.vector(b) #TRUE## [1] TRUEis.list(b)## [1] FALSEhead(b)## id scrape_id last_scraped name summary
## 0 0 0 0 0
## space
## 0b/5207>=0.80 #b/5207>=0.80 - missing## id
## FALSE
## scrape_id
## FALSE
## last_scraped
## FALSE
## name
## FALSE
## summary
## FALSE
## space
## FALSE
## description
## FALSE
## experiences_offered
## FALSE
## neighborhood_overview
## FALSE
## notes
## FALSE
## transit
## FALSE
## access
## FALSE
## interaction
## FALSE
## house_rules
## FALSE
## host_id
## FALSE
## host_name
## FALSE
## host_since
## FALSE
## host_location
## FALSE
## host_about
## FALSE
## host_response_time
## FALSE
## host_response_rate
## FALSE
## host_acceptance_rate
## FALSE
## host_is_superhost
## FALSE
## host_neighbourhood
## FALSE
## host_listings_count
## FALSE
## host_total_listings_count
## FALSE
## host_verifications
## FALSE
## host_has_profile_pic
## FALSE
## host_identity_verified
## FALSE
## street
## FALSE
## neighbourhood
## FALSE
## neighbourhood_cleansed
## FALSE
## neighbourhood_group_cleansed
## FALSE
## city
## FALSE
## state
## FALSE
## zipcode
## FALSE
## market
## FALSE
## smart_location
## FALSE
## country_code
## FALSE
## country
## FALSE
## latitude
## FALSE
## longitude
## FALSE
## is_location_exact
## FALSE
## property_type
## FALSE
## room_type
## FALSE
## accommodates
## FALSE
## bathrooms
## FALSE
## bedrooms
## FALSE
## beds
## FALSE
## bed_type
## FALSE
## amenities
## FALSE
## square_feet
## TRUE
## price
## FALSE
## weekly_price
## FALSE
## monthly_price
## FALSE
## security_deposit
## FALSE
## cleaning_fee
## FALSE
## guests_included
## FALSE
## extra_people
## FALSE
## minimum_nights
## FALSE
## maximum_nights
## FALSE
## minimum_minimum_nights
## FALSE
## maximum_minimum_nights
## FALSE
## minimum_maximum_nights
## FALSE
## maximum_maximum_nights
## FALSE
## minimum_nights_avg_ntm
## FALSE
## maximum_nights_avg_ntm
## FALSE
## calendar_updated
## FALSE
## has_availability
## FALSE
## availability_30
## FALSE
## availability_60
## FALSE
## availability_90
## FALSE
## availability_365
## FALSE
## calendar_last_scraped
## FALSE
## number_of_reviews
## FALSE
## number_of_reviews_ltm
## FALSE
## first_review
## FALSE
## last_review
## FALSE
## review_scores_rating
## TRUE
## review_scores_accuracy
## TRUE
## review_scores_cleanliness
## TRUE
## review_scores_checkin
## TRUE
## review_scores_communication
## TRUE
## review_scores_location
## TRUE
## review_scores_value
## TRUE
## requires_license
## FALSE
## license
## FALSE
## jurisdiction_names
## FALSE
## instant_bookable
## FALSE
## is_business_travel_ready
## FALSE
## cancellation_policy
## FALSE
## require_guest_profile_picture
## FALSE
## require_guest_phone_verification
## FALSE
## calculated_host_listings_count
## FALSE
## calculated_host_listings_count_entire_homes
## FALSE
## calculated_host_listings_count_private_rooms
## FALSE
## calculated_host_listings_count_shared_rooms
## FALSE
## reviews_per_month
## TRUEb[b/5207>=0.80] ## square_feet review_scores_rating
## 48487 11022
## review_scores_accuracy review_scores_cleanliness
## 11060 11043
## review_scores_checkin review_scores_communication
## 11078 11055
## review_scores_location review_scores_value
## 11082 11080
## reviews_per_month
## 10052names(b[b/5207>=0.80]) ## [1] "square_feet" "review_scores_rating"
## [3] "review_scores_accuracy" "review_scores_cleanliness"
## [5] "review_scores_checkin" "review_scores_communication"
## [7] "review_scores_location" "review_scores_value"
## [9] "reviews_per_month"data1[,names(b[b/5207>=0.80])] <- NULL
head(data1$square_feet)## NULLThis leads to a removal of variable Based on intuition after analyzing the data and further inspection we observed that below variables does not add any value to our analysis and should be removed. Also having performed EDA on the same dataset we have a more clear view of which variables contribute to predicting our target variable price
data1[,c("security_deposit","weekly_price","monthly_price","first_review","last_review","jurisdiction_names","zipcode","street","market","cleaning_fee","name","interaction","access","space","notes","description","host_name","host_has_profile_pic","host_verifications","host_neighborhood","require_guest_profile_picture","require_guest_phone_verification","calculated_host_listings_count", "host_location","transit","neighborhood_overview","house_rules","host_about","license", "requires_license","host_neighbourhood")] <- NULLRemoving variables with duplicated values
data1[,names(data1[(duplicated(t(data1)))])] <- NULLWe observed that different listings have different amenities and hence we created flag/dummy variables for the most important amenities we thought a user looks for while booking at Airbnb. These amenities are TV, Internet, Air Conditioning, Breakfast, Kitchen, and Pets. Adding amenities categories
a <- data1$amenities
data1$TV <- ifelse(grepl("TV",a, ignore.case = T)==T,1,0)
data1$Internet <- ifelse(grepl("Internet",a, ignore.case= T)==T,1,0)
data1$AirCondition <- ifelse(grepl("conditioning",a, ignore.case =T)==T,1,0)
data1$Pets <- ifelse(grepl("Pet",a, ignore.case = T)==T,1,0)
data1$Pets <- ifelse(grepl("Dog",a, ignore.case = T)==T,1,data1$Pets)
data1$Pets <- ifelse(grepl("Cat",a, ignore.case = T)==T,1,data1$Pets)
data1$Kitchen <- ifelse(grepl("Kitchen",a, ignore.case = T)==T,1,0)
data1$Breakfast <- ifelse(grepl("breakfast",a, ignore.case = T)==T,1,0)
data1[,c("amenities")] <- NULLThe data types of variables which we thought might be significant were converted to the appropriate data types. Converting price variable to integer
data1$price <- sub("\\$","",data1$price)
data1$price <- sub(",","",data1$price)
data1$price <- as.integer(data1$price)data1$host_response_time <- as.factor(data1$host_response_time)
data1$host_is_superhost <- as.factor(data1$host_is_superhost)
data1$host_identity_verified <- as.factor(data1$host_identity_verified)
data1$neighbourhood_cleansed <- as.factor(data1$neighbourhood_cleansed)
data1$is_location_exact <- as.factor(data1$is_location_exact)
data1$property_type <- as.factor(data1$property_type)
data1$room_type <- as.factor(data1$room_type)
data1$bed_type <- as.factor(data1$bed_type)
data1$calendar_updated <- as.factor(data1$calendar_updated)
data1$instant_bookable <- as.factor(data1$instant_bookable)
data1$cancellation_policy <- as.factor(data1$cancellation_policy)Treating host_response_rate and extra_people from character to a numeric variable for modeling purpose using SWOE method
data1$host_response_rate<- as.numeric(sub("%", "", data1$host_response_rate))## Warning: NAs introduced by coerciondata1$host_response_rate <- data1$host_response_rate/100
data1$extra_people <- as.numeric(sub("\\$","",data1$extra_people))Removing insignificant listings which have price = 0
data1 <- data1[-c(717,1334,3093),]We also created a variable about the number of days since the listing was on Airbnb
data1$host_since <- as.Date(data1$host_since)
data1$host_since <- as.Date("2017-05-10")-data1$host_sinceIn order to reduce the number of levels in factor variables with large number of levels, we clubbed the factors with low number of listings. In the dataset, we observed that neighbourhood_cleansed, which gives us an idea of the nighbourhood of the listing, has 72 levels.
a <- data1 %>% group_by(neighbourhood_cleansed) %>% summarise(len = length(neighbourhood_cleansed))
data1 <- merge(data1,a,by = "neighbourhood_cleansed")
data1$neighbourhood_cleansed <- as.character(data1$neighbourhood_cleansed)
data1$neighbourhood_cleansed <- ifelse(data1$len<150,"Others",data1$neighbourhood_cleansed)
data1$len <- NULLMissing Value Treatment checking missing values in different columns
missing = data.frame(col=colnames(data1), type = sapply(data1, class),
missing = sapply(data1, function(x) sum(is.na(x)| x=="" | x=="N/A"))/nrow(data1) * 100)Replace missing values in host_response_time with the most common occurring category (its mode)
data1$host_response_time<- sub("N/A","within an hour", data1$host_response_time)Replace missing values in host_response rate with mean values observed for host_response_time when it is within an hour
data1$host_response_rate <- ifelse(is.na(data1$host_response_rate)==T,0.99,data1$host_response_rate)Replace missing values in bathrooms, bedrooms and beds with the median value
data1$bathrooms <- ifelse(is.na(data1$bathrooms)==T, 1, data1$bathrooms)
data1$bedrooms <- ifelse(is.na(data1$bedrooms)==T,1,data1$bedrooms)
data1$beds <- ifelse(is.na(data1$beds)==T,1,data1$beds)Outliers Histogram of price
ggplot(data=data1, aes(price)) +
geom_histogram(fill="red") +
labs(title="Histogram of Price") +
labs(x="Price", y="Count")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Percentile of price
quantile(data1$price, c(.9, .95, .97, 0.975, 0.98, 0.99, 0.995, 0.999, 0.9999))## 90% 95% 97% 97.5% 98% 99% 99.5% 99.9% 99.99%
## 269 355 450 500 550 799 1000 3000 9999As we can see from the histogram as well as the percentile distribution of Price, there are extreme values in Price,So we performed Winsorization at 99% level and captured the maximum value of price at 650 USD. Capture the extreme values of Price at 99 percentile level
data1$price <- ifelse(data1$price>650,650,data1$price)
ggplot(data=data1, aes(price)) +
geom_histogram(fill="red") +
labs(title="Histogram of Price") +
labs(x="Price", y="Count")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Visualizing the distribution of no of reviews for different cancellation policy using a boxplot
ggboxplot(inp_data, x = "cancellation_policy", y = "number_of_reviews",
color = "cancellation_policy",
ylab = "Number of reviews", xlab = "Cancellation Policy")
Initial inspection of the data using the boxplot suggests that there are differences in the booking rates for different cancellation policies: the accommodations with Strict and Moderate cancellation policies have higher average booking rates (staying rate). To investigate for if there are significant differences among groups quantitatively, we then fited an one-way ANOVA model as below.
Compute the analysis of variance
anova1 <- aov(number_of_reviews ~ cancellation_policy, data = data1)Summary of the analysis
summary(anova1)## Df Sum Sq Mean Sq F value Pr(>F)
## cancellation_policy 5 4618644 923729 488.7 <2e-16 ***
## Residuals 48886 92408010 1890
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Since, p-value is less than the significance level 0.05 in the moel summary,we reject the null hypothesis and we can say that the number of reviews has an impact on cancellation policies.
names(data1)## [1] "neighbourhood_cleansed"
## [2] "id"
## [3] "scrape_id"
## [4] "last_scraped"
## [5] "summary"
## [6] "experiences_offered"
## [7] "host_id"
## [8] "host_since"
## [9] "host_response_time"
## [10] "host_response_rate"
## [11] "host_acceptance_rate"
## [12] "host_is_superhost"
## [13] "host_listings_count"
## [14] "host_identity_verified"
## [15] "neighbourhood"
## [16] "neighbourhood_group_cleansed"
## [17] "city"
## [18] "state"
## [19] "smart_location"
## [20] "country_code"
## [21] "country"
## [22] "latitude"
## [23] "longitude"
## [24] "is_location_exact"
## [25] "property_type"
## [26] "room_type"
## [27] "accommodates"
## [28] "bathrooms"
## [29] "bedrooms"
## [30] "beds"
## [31] "bed_type"
## [32] "price"
## [33] "guests_included"
## [34] "extra_people"
## [35] "minimum_nights"
## [36] "maximum_nights"
## [37] "minimum_minimum_nights"
## [38] "maximum_minimum_nights"
## [39] "minimum_maximum_nights"
## [40] "maximum_maximum_nights"
## [41] "minimum_nights_avg_ntm"
## [42] "maximum_nights_avg_ntm"
## [43] "calendar_updated"
## [44] "has_availability"
## [45] "availability_30"
## [46] "availability_60"
## [47] "availability_90"
## [48] "availability_365"
## [49] "number_of_reviews"
## [50] "number_of_reviews_ltm"
## [51] "instant_bookable"
## [52] "is_business_travel_ready"
## [53] "cancellation_policy"
## [54] "calculated_host_listings_count_entire_homes"
## [55] "calculated_host_listings_count_private_rooms"
## [56] "calculated_host_listings_count_shared_rooms"
## [57] "TV"
## [58] "Internet"
## [59] "AirCondition"
## [60] "Pets"
## [61] "Kitchen"
## [62] "Breakfast"Visualizing the distribution of review_scores_rating for different bed type using a boxplot
ggboxplot(data1, x = "bed_type", y = "price",
color = "bed_type",
ylab = "price", xlab = "Bed Type")
Initial inspection of the data using the boxplot suggests that there are no differences in the review rating for different bed types. To investigate for if there are differences among different groups quantitatively, we fitted an one-way ANOVA model.
Compute the analysis of variance
anova2 <- aov(price ~ bed_type, data = data1)Summary of the analysis
summary(anova2)## Df Sum Sq Mean Sq F value Pr(>F)
## bed_type 4 1209359 302340 23.66 <2e-16 ***
## Residuals 48887 624598530 12776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Since P-value is smaller than 0.05, we reject the null hypothesis that the price varies based on the bed types.
lm
data_size <- floor(0.8 * nrow(data1))
train_data <- sample(seq_len(nrow(data1)), size = data_size)
train <- data1[train_data, ]
test <- data1[-train_data, ]
names(data1)## [1] "neighbourhood_cleansed"
## [2] "id"
## [3] "scrape_id"
## [4] "last_scraped"
## [5] "summary"
## [6] "experiences_offered"
## [7] "host_id"
## [8] "host_since"
## [9] "host_response_time"
## [10] "host_response_rate"
## [11] "host_acceptance_rate"
## [12] "host_is_superhost"
## [13] "host_listings_count"
## [14] "host_identity_verified"
## [15] "neighbourhood"
## [16] "neighbourhood_group_cleansed"
## [17] "city"
## [18] "state"
## [19] "smart_location"
## [20] "country_code"
## [21] "country"
## [22] "latitude"
## [23] "longitude"
## [24] "is_location_exact"
## [25] "property_type"
## [26] "room_type"
## [27] "accommodates"
## [28] "bathrooms"
## [29] "bedrooms"
## [30] "beds"
## [31] "bed_type"
## [32] "price"
## [33] "guests_included"
## [34] "extra_people"
## [35] "minimum_nights"
## [36] "maximum_nights"
## [37] "minimum_minimum_nights"
## [38] "maximum_minimum_nights"
## [39] "minimum_maximum_nights"
## [40] "maximum_maximum_nights"
## [41] "minimum_nights_avg_ntm"
## [42] "maximum_nights_avg_ntm"
## [43] "calendar_updated"
## [44] "has_availability"
## [45] "availability_30"
## [46] "availability_60"
## [47] "availability_90"
## [48] "availability_365"
## [49] "number_of_reviews"
## [50] "number_of_reviews_ltm"
## [51] "instant_bookable"
## [52] "is_business_travel_ready"
## [53] "cancellation_policy"
## [54] "calculated_host_listings_count_entire_homes"
## [55] "calculated_host_listings_count_private_rooms"
## [56] "calculated_host_listings_count_shared_rooms"
## [57] "TV"
## [58] "Internet"
## [59] "AirCondition"
## [60] "Pets"
## [61] "Kitchen"
## [62] "Breakfast"price_model <- lm(price ~ as.factor(neighbourhood_cleansed) + host_since + Breakfast + Kitchen + Pets + AirCondition + Internet + TV + number_of_reviews + number_of_reviews_ltm + as.factor(cancellation_policy) + guests_included + beds + as.factor(bed_type) + bathrooms + as.factor(property_type) + as.factor(room_type),
data = train)
summary(price_model)##
## Call:
## lm(formula = price ~ as.factor(neighbourhood_cleansed) + host_since +
## Breakfast + Kitchen + Pets + AirCondition + Internet + TV +
## number_of_reviews + number_of_reviews_ltm + as.factor(cancellation_policy) +
## guests_included + beds + as.factor(bed_type) + bathrooms +
## as.factor(property_type) + as.factor(room_type), data = train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -897.08 -40.69 -8.83 22.35 666.27
##
## Coefficients:
## Estimate
## (Intercept) 1.050e+02
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant -1.347e+01
## as.factor(neighbourhood_cleansed)Boerum Hill 2.902e+01
## as.factor(neighbourhood_cleansed)Brooklyn Heights 6.426e+01
## as.factor(neighbourhood_cleansed)Bushwick -1.618e+01
## as.factor(neighbourhood_cleansed)Carroll Gardens 2.605e+01
## as.factor(neighbourhood_cleansed)Chelsea 8.778e+01
## as.factor(neighbourhood_cleansed)Chinatown 4.485e+01
## as.factor(neighbourhood_cleansed)Clinton Hill 2.259e+01
## as.factor(neighbourhood_cleansed)Crown Heights -9.491e+00
## as.factor(neighbourhood_cleansed)Ditmars Steinway -2.376e+00
## as.factor(neighbourhood_cleansed)East Elmhurst -1.835e+01
## as.factor(neighbourhood_cleansed)East Flatbush -2.287e+01
## as.factor(neighbourhood_cleansed)East Harlem 1.460e+01
## as.factor(neighbourhood_cleansed)East New York -2.916e+01
## as.factor(neighbourhood_cleansed)East Village 5.781e+01
## as.factor(neighbourhood_cleansed)Elmhurst -1.844e+01
## as.factor(neighbourhood_cleansed)Financial District 7.176e+01
## as.factor(neighbourhood_cleansed)Flatbush -1.412e+01
## as.factor(neighbourhood_cleansed)Flushing -1.378e+01
## as.factor(neighbourhood_cleansed)Fort Greene 2.498e+01
## as.factor(neighbourhood_cleansed)Gowanus 1.727e+01
## as.factor(neighbourhood_cleansed)Gramercy 7.035e+01
## as.factor(neighbourhood_cleansed)Greenpoint 1.473e+01
## as.factor(neighbourhood_cleansed)Greenwich Village 9.107e+01
## as.factor(neighbourhood_cleansed)Harlem 7.915e+00
## as.factor(neighbourhood_cleansed)Hell's Kitchen 7.500e+01
## as.factor(neighbourhood_cleansed)Inwood -1.292e+01
## as.factor(neighbourhood_cleansed)Jackson Heights -1.510e+01
## as.factor(neighbourhood_cleansed)Jamaica -2.251e+01
## as.factor(neighbourhood_cleansed)Kensington -1.831e+01
## as.factor(neighbourhood_cleansed)Kips Bay 5.978e+01
## as.factor(neighbourhood_cleansed)Long Island City 1.430e+01
## as.factor(neighbourhood_cleansed)Lower East Side 5.227e+01
## as.factor(neighbourhood_cleansed)Midtown 1.084e+02
## as.factor(neighbourhood_cleansed)Morningside Heights 1.285e+01
## as.factor(neighbourhood_cleansed)Murray Hill 6.968e+01
## as.factor(neighbourhood_cleansed)Nolita 8.684e+01
## as.factor(neighbourhood_cleansed)Others -6.102e+00
## as.factor(neighbourhood_cleansed)Park Slope 2.499e+01
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens -1.661e+01
## as.factor(neighbourhood_cleansed)Prospect Heights 2.434e+01
## as.factor(neighbourhood_cleansed)Ridgewood -2.747e+01
## as.factor(neighbourhood_cleansed)Sheepshead Bay -1.538e+01
## as.factor(neighbourhood_cleansed)SoHo 1.193e+02
## as.factor(neighbourhood_cleansed)South Slope 9.181e+00
## as.factor(neighbourhood_cleansed)Sunnyside -1.244e+01
## as.factor(neighbourhood_cleansed)Sunset Park -8.644e+00
## as.factor(neighbourhood_cleansed)Theater District 9.229e+01
## as.factor(neighbourhood_cleansed)Tribeca 1.659e+02
## as.factor(neighbourhood_cleansed)Upper East Side 4.598e+01
## as.factor(neighbourhood_cleansed)Upper West Side 5.519e+01
## as.factor(neighbourhood_cleansed)Washington Heights -4.261e+00
## as.factor(neighbourhood_cleansed)West Village 1.061e+02
## as.factor(neighbourhood_cleansed)Williamsburg 2.791e+01
## as.factor(neighbourhood_cleansed)Windsor Terrace -4.090e+00
## as.factor(neighbourhood_cleansed)Woodside -1.287e+01
## host_since -1.627e-03
## Breakfast 1.364e+01
## Kitchen -9.412e+00
## Pets 6.244e+00
## AirCondition 3.324e+00
## Internet 8.808e-01
## TV 1.151e+01
## number_of_reviews -4.102e-02
## number_of_reviews_ltm -3.419e-01
## as.factor(cancellation_policy)moderate -9.295e+00
## as.factor(cancellation_policy)strict -6.083e+01
## as.factor(cancellation_policy)strict_14_with_grace_period -4.209e+00
## as.factor(cancellation_policy)super_strict_30 -8.605e+01
## as.factor(cancellation_policy)super_strict_60 4.943e+01
## guests_included 1.230e+01
## beds 1.899e+01
## as.factor(bed_type)Couch 1.548e+01
## as.factor(bed_type)Futon -1.200e+01
## as.factor(bed_type)Pull-out Sofa -1.235e+01
## as.factor(bed_type)Real Bed -1.058e+01
## bathrooms 4.725e+01
## as.factor(property_type)Apartment -4.504e+01
## as.factor(property_type)Barn -2.377e+01
## as.factor(property_type)Bed and breakfast -3.605e+01
## as.factor(property_type)Boat 5.979e+01
## as.factor(property_type)Boutique hotel 5.532e+01
## as.factor(property_type)Bungalow -5.200e+00
## as.factor(property_type)Bus -4.859e+00
## as.factor(property_type)Cabin 2.908e+00
## as.factor(property_type)Camper/RV -1.056e+02
## as.factor(property_type)Casa particular (Cuba) -4.155e+01
## as.factor(property_type)Castle -2.996e+01
## as.factor(property_type)Cave 1.731e+01
## as.factor(property_type)Condominium -9.800e+00
## as.factor(property_type)Cottage -5.562e+01
## as.factor(property_type)Dome house -5.925e+01
## as.factor(property_type)Earth house -1.085e+02
## as.factor(property_type)Farm stay -4.341e+01
## as.factor(property_type)Guest suite -5.898e+01
## as.factor(property_type)Guesthouse -3.762e+01
## as.factor(property_type)Hostel -9.385e+01
## as.factor(property_type)Hotel -2.554e+01
## as.factor(property_type)House -4.328e+01
## as.factor(property_type)Houseboat 1.039e+02
## as.factor(property_type)Loft 1.396e+00
## as.factor(property_type)Nature lodge -5.047e+01
## as.factor(property_type)Other -1.949e+01
## as.factor(property_type)Resort 1.730e+02
## as.factor(property_type)Serviced apartment -2.445e+01
## as.factor(property_type)Tent 3.713e+01
## as.factor(property_type)Tiny house -5.363e+01
## as.factor(property_type)Townhouse -3.397e+01
## as.factor(property_type)Villa -2.875e+00
## as.factor(property_type)Yurt -7.172e+01
## as.factor(room_type)Private room -6.588e+01
## as.factor(room_type)Shared room -1.001e+02
## Std. Error
## (Intercept) 2.539e+01
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant 3.317e+00
## as.factor(neighbourhood_cleansed)Boerum Hill 7.306e+00
## as.factor(neighbourhood_cleansed)Brooklyn Heights 7.677e+00
## as.factor(neighbourhood_cleansed)Bushwick 3.476e+00
## as.factor(neighbourhood_cleansed)Carroll Gardens 6.529e+00
## as.factor(neighbourhood_cleansed)Chelsea 4.028e+00
## as.factor(neighbourhood_cleansed)Chinatown 5.555e+00
## as.factor(neighbourhood_cleansed)Clinton Hill 4.798e+00
## as.factor(neighbourhood_cleansed)Crown Heights 3.742e+00
## as.factor(neighbourhood_cleansed)Ditmars Steinway 5.949e+00
## as.factor(neighbourhood_cleansed)East Elmhurst 7.355e+00
## as.factor(neighbourhood_cleansed)East Flatbush 5.045e+00
## as.factor(neighbourhood_cleansed)East Harlem 4.001e+00
## as.factor(neighbourhood_cleansed)East New York 6.848e+00
## as.factor(neighbourhood_cleansed)East Village 3.637e+00
## as.factor(neighbourhood_cleansed)Elmhurst 6.487e+00
## as.factor(neighbourhood_cleansed)Financial District 4.415e+00
## as.factor(neighbourhood_cleansed)Flatbush 4.581e+00
## as.factor(neighbourhood_cleansed)Flushing 5.283e+00
## as.factor(neighbourhood_cleansed)Fort Greene 5.033e+00
## as.factor(neighbourhood_cleansed)Gowanus 6.338e+00
## as.factor(neighbourhood_cleansed)Gramercy 5.700e+00
## as.factor(neighbourhood_cleansed)Greenpoint 3.986e+00
## as.factor(neighbourhood_cleansed)Greenwich Village 5.374e+00
## as.factor(neighbourhood_cleansed)Harlem 3.438e+00
## as.factor(neighbourhood_cleansed)Hell's Kitchen 3.607e+00
## as.factor(neighbourhood_cleansed)Inwood 6.355e+00
## as.factor(neighbourhood_cleansed)Jackson Heights 7.317e+00
## as.factor(neighbourhood_cleansed)Jamaica 6.672e+00
## as.factor(neighbourhood_cleansed)Kensington 7.322e+00
## as.factor(neighbourhood_cleansed)Kips Bay 5.114e+00
## as.factor(neighbourhood_cleansed)Long Island City 4.983e+00
## as.factor(neighbourhood_cleansed)Lower East Side 4.189e+00
## as.factor(neighbourhood_cleansed)Midtown 3.829e+00
## as.factor(neighbourhood_cleansed)Morningside Heights 5.648e+00
## as.factor(neighbourhood_cleansed)Murray Hill 5.005e+00
## as.factor(neighbourhood_cleansed)Nolita 6.504e+00
## as.factor(neighbourhood_cleansed)Others 3.231e+00
## as.factor(neighbourhood_cleansed)Park Slope 4.956e+00
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens 4.863e+00
## as.factor(neighbourhood_cleansed)Prospect Heights 5.655e+00
## as.factor(neighbourhood_cleansed)Ridgewood 5.272e+00
## as.factor(neighbourhood_cleansed)Sheepshead Bay 7.495e+00
## as.factor(neighbourhood_cleansed)SoHo 5.638e+00
## as.factor(neighbourhood_cleansed)South Slope 5.998e+00
## as.factor(neighbourhood_cleansed)Sunnyside 5.535e+00
## as.factor(neighbourhood_cleansed)Sunset Park 5.363e+00
## as.factor(neighbourhood_cleansed)Theater District 6.146e+00
## as.factor(neighbourhood_cleansed)Tribeca 7.544e+00
## as.factor(neighbourhood_cleansed)Upper East Side 3.664e+00
## as.factor(neighbourhood_cleansed)Upper West Side 3.603e+00
## as.factor(neighbourhood_cleansed)Washington Heights 4.192e+00
## as.factor(neighbourhood_cleansed)West Village 4.418e+00
## as.factor(neighbourhood_cleansed)Williamsburg 3.306e+00
## as.factor(neighbourhood_cleansed)Windsor Terrace 7.677e+00
## as.factor(neighbourhood_cleansed)Woodside 6.438e+00
## host_since 5.571e-04
## Breakfast 1.517e+00
## Kitchen 1.569e+00
## Pets 1.065e+00
## AirCondition 1.199e+00
## Internet 9.904e-01
## TV 9.349e-01
## number_of_reviews 1.572e-02
## number_of_reviews_ltm 4.287e-02
## as.factor(cancellation_policy)moderate 1.145e+00
## as.factor(cancellation_policy)strict 5.658e+01
## as.factor(cancellation_policy)strict_14_with_grace_period 9.961e-01
## as.factor(cancellation_policy)super_strict_30 1.898e+01
## as.factor(cancellation_policy)super_strict_60 8.445e+00
## guests_included 4.173e-01
## beds 4.657e-01
## as.factor(bed_type)Couch 1.306e+01
## as.factor(bed_type)Futon 8.559e+00
## as.factor(bed_type)Pull-out Sofa 8.818e+00
## as.factor(bed_type)Real Bed 6.850e+00
## bathrooms 1.036e+00
## as.factor(property_type)Apartment 2.420e+01
## as.factor(property_type)Barn 8.361e+01
## as.factor(property_type)Bed and breakfast 2.594e+01
## as.factor(property_type)Boat 3.605e+01
## as.factor(property_type)Boutique hotel 2.512e+01
## as.factor(property_type)Bungalow 2.943e+01
## as.factor(property_type)Bus 8.358e+01
## as.factor(property_type)Cabin 4.681e+01
## as.factor(property_type)Camper/RV 3.423e+01
## as.factor(property_type)Casa particular (Cuba) 5.219e+01
## as.factor(property_type)Castle 8.359e+01
## as.factor(property_type)Cave 6.153e+01
## as.factor(property_type)Condominium 2.430e+01
## as.factor(property_type)Cottage 5.215e+01
## as.factor(property_type)Dome house 6.152e+01
## as.factor(property_type)Earth house 5.217e+01
## as.factor(property_type)Farm stay 6.157e+01
## as.factor(property_type)Guest suite 2.469e+01
## as.factor(property_type)Guesthouse 2.682e+01
## as.factor(property_type)Hostel 2.654e+01
## as.factor(property_type)Hotel 2.489e+01
## as.factor(property_type)House 2.425e+01
## as.factor(property_type)Houseboat 5.217e+01
## as.factor(property_type)Loft 2.432e+01
## as.factor(property_type)Nature lodge 8.357e+01
## as.factor(property_type)Other 2.544e+01
## as.factor(property_type)Resort 2.619e+01
## as.factor(property_type)Serviced apartment 2.450e+01
## as.factor(property_type)Tent 4.677e+01
## as.factor(property_type)Tiny house 3.041e+01
## as.factor(property_type)Townhouse 2.431e+01
## as.factor(property_type)Villa 2.995e+01
## as.factor(property_type)Yurt 6.153e+01
## as.factor(room_type)Private room 9.792e-01
## as.factor(room_type)Shared room 2.799e+00
## t value
## (Intercept) 4.135
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant -4.061
## as.factor(neighbourhood_cleansed)Boerum Hill 3.972
## as.factor(neighbourhood_cleansed)Brooklyn Heights 8.370
## as.factor(neighbourhood_cleansed)Bushwick -4.654
## as.factor(neighbourhood_cleansed)Carroll Gardens 3.989
## as.factor(neighbourhood_cleansed)Chelsea 21.794
## as.factor(neighbourhood_cleansed)Chinatown 8.075
## as.factor(neighbourhood_cleansed)Clinton Hill 4.708
## as.factor(neighbourhood_cleansed)Crown Heights -2.536
## as.factor(neighbourhood_cleansed)Ditmars Steinway -0.399
## as.factor(neighbourhood_cleansed)East Elmhurst -2.495
## as.factor(neighbourhood_cleansed)East Flatbush -4.534
## as.factor(neighbourhood_cleansed)East Harlem 3.648
## as.factor(neighbourhood_cleansed)East New York -4.258
## as.factor(neighbourhood_cleansed)East Village 15.893
## as.factor(neighbourhood_cleansed)Elmhurst -2.843
## as.factor(neighbourhood_cleansed)Financial District 16.253
## as.factor(neighbourhood_cleansed)Flatbush -3.081
## as.factor(neighbourhood_cleansed)Flushing -2.608
## as.factor(neighbourhood_cleansed)Fort Greene 4.963
## as.factor(neighbourhood_cleansed)Gowanus 2.725
## as.factor(neighbourhood_cleansed)Gramercy 12.341
## as.factor(neighbourhood_cleansed)Greenpoint 3.696
## as.factor(neighbourhood_cleansed)Greenwich Village 16.945
## as.factor(neighbourhood_cleansed)Harlem 2.302
## as.factor(neighbourhood_cleansed)Hell's Kitchen 20.791
## as.factor(neighbourhood_cleansed)Inwood -2.034
## as.factor(neighbourhood_cleansed)Jackson Heights -2.064
## as.factor(neighbourhood_cleansed)Jamaica -3.374
## as.factor(neighbourhood_cleansed)Kensington -2.501
## as.factor(neighbourhood_cleansed)Kips Bay 11.689
## as.factor(neighbourhood_cleansed)Long Island City 2.869
## as.factor(neighbourhood_cleansed)Lower East Side 12.480
## as.factor(neighbourhood_cleansed)Midtown 28.321
## as.factor(neighbourhood_cleansed)Morningside Heights 2.275
## as.factor(neighbourhood_cleansed)Murray Hill 13.921
## as.factor(neighbourhood_cleansed)Nolita 13.353
## as.factor(neighbourhood_cleansed)Others -1.889
## as.factor(neighbourhood_cleansed)Park Slope 5.042
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens -3.417
## as.factor(neighbourhood_cleansed)Prospect Heights 4.304
## as.factor(neighbourhood_cleansed)Ridgewood -5.210
## as.factor(neighbourhood_cleansed)Sheepshead Bay -2.052
## as.factor(neighbourhood_cleansed)SoHo 21.164
## as.factor(neighbourhood_cleansed)South Slope 1.531
## as.factor(neighbourhood_cleansed)Sunnyside -2.247
## as.factor(neighbourhood_cleansed)Sunset Park -1.612
## as.factor(neighbourhood_cleansed)Theater District 15.016
## as.factor(neighbourhood_cleansed)Tribeca 21.995
## as.factor(neighbourhood_cleansed)Upper East Side 12.550
## as.factor(neighbourhood_cleansed)Upper West Side 15.319
## as.factor(neighbourhood_cleansed)Washington Heights -1.016
## as.factor(neighbourhood_cleansed)West Village 24.007
## as.factor(neighbourhood_cleansed)Williamsburg 8.444
## as.factor(neighbourhood_cleansed)Windsor Terrace -0.533
## as.factor(neighbourhood_cleansed)Woodside -2.000
## host_since -2.921
## Breakfast 8.993
## Kitchen -5.997
## Pets 5.866
## AirCondition 2.771
## Internet 0.889
## TV 12.312
## number_of_reviews -2.609
## number_of_reviews_ltm -7.975
## as.factor(cancellation_policy)moderate -8.116
## as.factor(cancellation_policy)strict -1.075
## as.factor(cancellation_policy)strict_14_with_grace_period -4.226
## as.factor(cancellation_policy)super_strict_30 -4.534
## as.factor(cancellation_policy)super_strict_60 5.854
## guests_included 29.467
## beds 40.787
## as.factor(bed_type)Couch 1.185
## as.factor(bed_type)Futon -1.402
## as.factor(bed_type)Pull-out Sofa -1.401
## as.factor(bed_type)Real Bed -1.545
## bathrooms 45.617
## as.factor(property_type)Apartment -1.861
## as.factor(property_type)Barn -0.284
## as.factor(property_type)Bed and breakfast -1.390
## as.factor(property_type)Boat 1.659
## as.factor(property_type)Boutique hotel 2.202
## as.factor(property_type)Bungalow -0.177
## as.factor(property_type)Bus -0.058
## as.factor(property_type)Cabin 0.062
## as.factor(property_type)Camper/RV -3.086
## as.factor(property_type)Casa particular (Cuba) -0.796
## as.factor(property_type)Castle -0.358
## as.factor(property_type)Cave 0.281
## as.factor(property_type)Condominium -0.403
## as.factor(property_type)Cottage -1.066
## as.factor(property_type)Dome house -0.963
## as.factor(property_type)Earth house -2.079
## as.factor(property_type)Farm stay -0.705
## as.factor(property_type)Guest suite -2.388
## as.factor(property_type)Guesthouse -1.402
## as.factor(property_type)Hostel -3.536
## as.factor(property_type)Hotel -1.026
## as.factor(property_type)House -1.785
## as.factor(property_type)Houseboat 1.992
## as.factor(property_type)Loft 0.057
## as.factor(property_type)Nature lodge -0.604
## as.factor(property_type)Other -0.766
## as.factor(property_type)Resort 6.607
## as.factor(property_type)Serviced apartment -0.998
## as.factor(property_type)Tent 0.794
## as.factor(property_type)Tiny house -1.763
## as.factor(property_type)Townhouse -1.398
## as.factor(property_type)Villa -0.096
## as.factor(property_type)Yurt -1.166
## as.factor(room_type)Private room -67.281
## as.factor(room_type)Shared room -35.773
## Pr(>|t|)
## (Intercept) 3.55e-05 ***
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant 4.90e-05 ***
## as.factor(neighbourhood_cleansed)Boerum Hill 7.15e-05 ***
## as.factor(neighbourhood_cleansed)Brooklyn Heights < 2e-16 ***
## as.factor(neighbourhood_cleansed)Bushwick 3.27e-06 ***
## as.factor(neighbourhood_cleansed)Carroll Gardens 6.64e-05 ***
## as.factor(neighbourhood_cleansed)Chelsea < 2e-16 ***
## as.factor(neighbourhood_cleansed)Chinatown 6.95e-16 ***
## as.factor(neighbourhood_cleansed)Clinton Hill 2.51e-06 ***
## as.factor(neighbourhood_cleansed)Crown Heights 0.011208 *
## as.factor(neighbourhood_cleansed)Ditmars Steinway 0.689635
## as.factor(neighbourhood_cleansed)East Elmhurst 0.012603 *
## as.factor(neighbourhood_cleansed)East Flatbush 5.80e-06 ***
## as.factor(neighbourhood_cleansed)East Harlem 0.000264 ***
## as.factor(neighbourhood_cleansed)East New York 2.07e-05 ***
## as.factor(neighbourhood_cleansed)East Village < 2e-16 ***
## as.factor(neighbourhood_cleansed)Elmhurst 0.004471 **
## as.factor(neighbourhood_cleansed)Financial District < 2e-16 ***
## as.factor(neighbourhood_cleansed)Flatbush 0.002062 **
## as.factor(neighbourhood_cleansed)Flushing 0.009104 **
## as.factor(neighbourhood_cleansed)Fort Greene 6.97e-07 ***
## as.factor(neighbourhood_cleansed)Gowanus 0.006438 **
## as.factor(neighbourhood_cleansed)Gramercy < 2e-16 ***
## as.factor(neighbourhood_cleansed)Greenpoint 0.000220 ***
## as.factor(neighbourhood_cleansed)Greenwich Village < 2e-16 ***
## as.factor(neighbourhood_cleansed)Harlem 0.021313 *
## as.factor(neighbourhood_cleansed)Hell's Kitchen < 2e-16 ***
## as.factor(neighbourhood_cleansed)Inwood 0.041981 *
## as.factor(neighbourhood_cleansed)Jackson Heights 0.039054 *
## as.factor(neighbourhood_cleansed)Jamaica 0.000742 ***
## as.factor(neighbourhood_cleansed)Kensington 0.012394 *
## as.factor(neighbourhood_cleansed)Kips Bay < 2e-16 ***
## as.factor(neighbourhood_cleansed)Long Island City 0.004118 **
## as.factor(neighbourhood_cleansed)Lower East Side < 2e-16 ***
## as.factor(neighbourhood_cleansed)Midtown < 2e-16 ***
## as.factor(neighbourhood_cleansed)Morningside Heights 0.022890 *
## as.factor(neighbourhood_cleansed)Murray Hill < 2e-16 ***
## as.factor(neighbourhood_cleansed)Nolita < 2e-16 ***
## as.factor(neighbourhood_cleansed)Others 0.058937 .
## as.factor(neighbourhood_cleansed)Park Slope 4.62e-07 ***
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens 0.000635 ***
## as.factor(neighbourhood_cleansed)Prospect Heights 1.68e-05 ***
## as.factor(neighbourhood_cleansed)Ridgewood 1.89e-07 ***
## as.factor(neighbourhood_cleansed)Sheepshead Bay 0.040203 *
## as.factor(neighbourhood_cleansed)SoHo < 2e-16 ***
## as.factor(neighbourhood_cleansed)South Slope 0.125883
## as.factor(neighbourhood_cleansed)Sunnyside 0.024674 *
## as.factor(neighbourhood_cleansed)Sunset Park 0.107045
## as.factor(neighbourhood_cleansed)Theater District < 2e-16 ***
## as.factor(neighbourhood_cleansed)Tribeca < 2e-16 ***
## as.factor(neighbourhood_cleansed)Upper East Side < 2e-16 ***
## as.factor(neighbourhood_cleansed)Upper West Side < 2e-16 ***
## as.factor(neighbourhood_cleansed)Washington Heights 0.309414
## as.factor(neighbourhood_cleansed)West Village < 2e-16 ***
## as.factor(neighbourhood_cleansed)Williamsburg < 2e-16 ***
## as.factor(neighbourhood_cleansed)Windsor Terrace 0.594172
## as.factor(neighbourhood_cleansed)Woodside 0.045528 *
## host_since 0.003490 **
## Breakfast < 2e-16 ***
## Kitchen 2.02e-09 ***
## Pets 4.51e-09 ***
## AirCondition 0.005588 **
## Internet 0.373812
## TV < 2e-16 ***
## number_of_reviews 0.009095 **
## number_of_reviews_ltm 1.56e-15 ***
## as.factor(cancellation_policy)moderate 4.95e-16 ***
## as.factor(cancellation_policy)strict 0.282340
## as.factor(cancellation_policy)strict_14_with_grace_period 2.39e-05 ***
## as.factor(cancellation_policy)super_strict_30 5.81e-06 ***
## as.factor(cancellation_policy)super_strict_60 4.84e-09 ***
## guests_included < 2e-16 ***
## beds < 2e-16 ***
## as.factor(bed_type)Couch 0.236176
## as.factor(bed_type)Futon 0.161028
## as.factor(bed_type)Pull-out Sofa 0.161329
## as.factor(bed_type)Real Bed 0.122473
## bathrooms < 2e-16 ***
## as.factor(property_type)Apartment 0.062735 .
## as.factor(property_type)Barn 0.776217
## as.factor(property_type)Bed and breakfast 0.164616
## as.factor(property_type)Boat 0.097190 .
## as.factor(property_type)Boutique hotel 0.027637 *
## as.factor(property_type)Bungalow 0.859725
## as.factor(property_type)Bus 0.953635
## as.factor(property_type)Cabin 0.950474
## as.factor(property_type)Camper/RV 0.002029 **
## as.factor(property_type)Casa particular (Cuba) 0.425922
## as.factor(property_type)Castle 0.720069
## as.factor(property_type)Cave 0.778476
## as.factor(property_type)Condominium 0.686773
## as.factor(property_type)Cottage 0.286260
## as.factor(property_type)Dome house 0.335502
## as.factor(property_type)Earth house 0.037582 *
## as.factor(property_type)Farm stay 0.480849
## as.factor(property_type)Guest suite 0.016926 *
## as.factor(property_type)Guesthouse 0.160790
## as.factor(property_type)Hostel 0.000407 ***
## as.factor(property_type)Hotel 0.304964
## as.factor(property_type)House 0.074343 .
## as.factor(property_type)Houseboat 0.046355 *
## as.factor(property_type)Loft 0.954239
## as.factor(property_type)Nature lodge 0.545875
## as.factor(property_type)Other 0.443688
## as.factor(property_type)Resort 3.97e-11 ***
## as.factor(property_type)Serviced apartment 0.318136
## as.factor(property_type)Tent 0.427270
## as.factor(property_type)Tiny house 0.077851 .
## as.factor(property_type)Townhouse 0.162198
## as.factor(property_type)Villa 0.923539
## as.factor(property_type)Yurt 0.243791
## as.factor(room_type)Private room < 2e-16 ***
## as.factor(room_type)Shared room < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 79.97 on 38985 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.5073, Adjusted R-squared: 0.5059
## F-statistic: 358.4 on 112 and 38985 DF, p-value: < 2.2e-16baseline_model <- mean(data1$price)
(RMSE <- sqrt(mean(baseline_model- test$price)^2))## [1] 0.458891coefficients(price_model)## (Intercept)
## 1.050055e+02
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant
## -1.346971e+01
## as.factor(neighbourhood_cleansed)Boerum Hill
## 2.901518e+01
## as.factor(neighbourhood_cleansed)Brooklyn Heights
## 6.425522e+01
## as.factor(neighbourhood_cleansed)Bushwick
## -1.617526e+01
## as.factor(neighbourhood_cleansed)Carroll Gardens
## 2.604542e+01
## as.factor(neighbourhood_cleansed)Chelsea
## 8.777666e+01
## as.factor(neighbourhood_cleansed)Chinatown
## 4.485293e+01
## as.factor(neighbourhood_cleansed)Clinton Hill
## 2.258685e+01
## as.factor(neighbourhood_cleansed)Crown Heights
## -9.491254e+00
## as.factor(neighbourhood_cleansed)Ditmars Steinway
## -2.375590e+00
## as.factor(neighbourhood_cleansed)East Elmhurst
## -1.835129e+01
## as.factor(neighbourhood_cleansed)East Flatbush
## -2.287327e+01
## as.factor(neighbourhood_cleansed)East Harlem
## 1.459749e+01
## as.factor(neighbourhood_cleansed)East New York
## -2.915902e+01
## as.factor(neighbourhood_cleansed)East Village
## 5.780695e+01
## as.factor(neighbourhood_cleansed)Elmhurst
## -1.844210e+01
## as.factor(neighbourhood_cleansed)Financial District
## 7.176295e+01
## as.factor(neighbourhood_cleansed)Flatbush
## -1.411639e+01
## as.factor(neighbourhood_cleansed)Flushing
## -1.377921e+01
## as.factor(neighbourhood_cleansed)Fort Greene
## 2.498015e+01
## as.factor(neighbourhood_cleansed)Gowanus
## 1.726996e+01
## as.factor(neighbourhood_cleansed)Gramercy
## 7.034543e+01
## as.factor(neighbourhood_cleansed)Greenpoint
## 1.473227e+01
## as.factor(neighbourhood_cleansed)Greenwich Village
## 9.106886e+01
## as.factor(neighbourhood_cleansed)Harlem
## 7.915236e+00
## as.factor(neighbourhood_cleansed)Hell's Kitchen
## 7.500068e+01
## as.factor(neighbourhood_cleansed)Inwood
## -1.292462e+01
## as.factor(neighbourhood_cleansed)Jackson Heights
## -1.510089e+01
## as.factor(neighbourhood_cleansed)Jamaica
## -2.251171e+01
## as.factor(neighbourhood_cleansed)Kensington
## -1.831082e+01
## as.factor(neighbourhood_cleansed)Kips Bay
## 5.977948e+01
## as.factor(neighbourhood_cleansed)Long Island City
## 1.429802e+01
## as.factor(neighbourhood_cleansed)Lower East Side
## 5.227282e+01
## as.factor(neighbourhood_cleansed)Midtown
## 1.084366e+02
## as.factor(neighbourhood_cleansed)Morningside Heights
## 1.285209e+01
## as.factor(neighbourhood_cleansed)Murray Hill
## 6.967759e+01
## as.factor(neighbourhood_cleansed)Nolita
## 8.684186e+01
## as.factor(neighbourhood_cleansed)Others
## -6.101992e+00
## as.factor(neighbourhood_cleansed)Park Slope
## 2.498743e+01
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens
## -1.661415e+01
## as.factor(neighbourhood_cleansed)Prospect Heights
## 2.433667e+01
## as.factor(neighbourhood_cleansed)Ridgewood
## -2.746752e+01
## as.factor(neighbourhood_cleansed)Sheepshead Bay
## -1.537743e+01
## as.factor(neighbourhood_cleansed)SoHo
## 1.193304e+02
## as.factor(neighbourhood_cleansed)South Slope
## 9.180979e+00
## as.factor(neighbourhood_cleansed)Sunnyside
## -1.243553e+01
## as.factor(neighbourhood_cleansed)Sunset Park
## -8.643831e+00
## as.factor(neighbourhood_cleansed)Theater District
## 9.228847e+01
## as.factor(neighbourhood_cleansed)Tribeca
## 1.659250e+02
## as.factor(neighbourhood_cleansed)Upper East Side
## 4.597892e+01
## as.factor(neighbourhood_cleansed)Upper West Side
## 5.518816e+01
## as.factor(neighbourhood_cleansed)Washington Heights
## -4.261390e+00
## as.factor(neighbourhood_cleansed)West Village
## 1.060733e+02
## as.factor(neighbourhood_cleansed)Williamsburg
## 2.791361e+01
## as.factor(neighbourhood_cleansed)Windsor Terrace
## -4.090477e+00
## as.factor(neighbourhood_cleansed)Woodside
## -1.287384e+01
## host_since
## -1.627348e-03
## Breakfast
## 1.364022e+01
## Kitchen
## -9.411602e+00
## Pets
## 6.244216e+00
## AirCondition
## 3.323862e+00
## Internet
## 8.808001e-01
## TV
## 1.151012e+01
## number_of_reviews
## -4.101610e-02
## number_of_reviews_ltm
## -3.419255e-01
## as.factor(cancellation_policy)moderate
## -9.294667e+00
## as.factor(cancellation_policy)strict
## -6.082563e+01
## as.factor(cancellation_policy)strict_14_with_grace_period
## -4.209245e+00
## as.factor(cancellation_policy)super_strict_30
## -8.605045e+01
## as.factor(cancellation_policy)super_strict_60
## 4.943350e+01
## guests_included
## 1.229666e+01
## beds
## 1.899331e+01
## as.factor(bed_type)Couch
## 1.547594e+01
## as.factor(bed_type)Futon
## -1.199636e+01
## as.factor(bed_type)Pull-out Sofa
## -1.235107e+01
## as.factor(bed_type)Real Bed
## -1.058017e+01
## bathrooms
## 4.725458e+01
## as.factor(property_type)Apartment
## -4.504420e+01
## as.factor(property_type)Barn
## -2.376750e+01
## as.factor(property_type)Bed and breakfast
## -3.604807e+01
## as.factor(property_type)Boat
## 5.979156e+01
## as.factor(property_type)Boutique hotel
## 5.532361e+01
## as.factor(property_type)Bungalow
## -5.200307e+00
## as.factor(property_type)Bus
## -4.859472e+00
## as.factor(property_type)Cabin
## 2.907508e+00
## as.factor(property_type)Camper/RV
## -1.056458e+02
## as.factor(property_type)Casa particular (Cuba)
## -4.155418e+01
## as.factor(property_type)Castle
## -2.995574e+01
## as.factor(property_type)Cave
## 1.730750e+01
## as.factor(property_type)Condominium
## -9.800311e+00
## as.factor(property_type)Cottage
## -5.561619e+01
## as.factor(property_type)Dome house
## -5.925228e+01
## as.factor(property_type)Earth house
## -1.084893e+02
## as.factor(property_type)Farm stay
## -4.340543e+01
## as.factor(property_type)Guest suite
## -5.897736e+01
## as.factor(property_type)Guesthouse
## -3.761633e+01
## as.factor(property_type)Hostel
## -9.384919e+01
## as.factor(property_type)Hotel
## -2.553606e+01
## as.factor(property_type)House
## -4.327916e+01
## as.factor(property_type)Houseboat
## 1.039297e+02
## as.factor(property_type)Loft
## 1.395668e+00
## as.factor(property_type)Nature lodge
## -5.047377e+01
## as.factor(property_type)Other
## -1.948960e+01
## as.factor(property_type)Resort
## 1.730388e+02
## as.factor(property_type)Serviced apartment
## -2.445414e+01
## as.factor(property_type)Tent
## 3.712826e+01
## as.factor(property_type)Tiny house
## -5.362854e+01
## as.factor(property_type)Townhouse
## -3.397475e+01
## as.factor(property_type)Villa
## -2.874806e+00
## as.factor(property_type)Yurt
## -7.172294e+01
## as.factor(room_type)Private room
## -6.588109e+01
## as.factor(room_type)Shared room
## -1.001208e+02Following are the results that we obtained from the above linear regression model along with the hypothesis/explanations of behaviors of the variables:
Based on the p-values for the above regression model, we can say that amenities like TV and Air Conditioning significantly impact the prices. Since the Beta-values are positive, we can say that TV and AC have positive effects on prices (considering other variables are fixed). This can be because AC and TV are comparatively expensive amenties
Also, the booking rate is inversely proportional to the prices considering the other factors remain unchanged Number of guests, number of beds, and number of bathrooms directly affect the prices. As the number of guests, beds or bathrooms increases, prices increase (considering amenities/comfort and location is same). This is reasonable and hotels share the same theory.
The categorical variables: neighbourhood, property type, and room type have significant impacts on prices. This is generally true as properties in luxurious localities will be more expensive than other housing areas where provide similar services. Also, a shared room will be cheaper than a whole house/apartment in the same locality.**
XGBoost
target_train=train$price
target_test=test$price
str(target_train)## num [1:39113] 55 200 60 95 100 90 75 90 200 55 ...str(train)## 'data.frame': 39113 obs. of 62 variables:
## $ neighbourhood_cleansed : chr "Others" "Fort Greene" "Washington Heights" "Williamsburg" ...
## $ id : int 27510869 28289858 7973933 9546695 7680750 14265052 5524950 22987769 21241829 29584983 ...
## $ scrape_id : num 2.02e+13 2.02e+13 2.02e+13 2.02e+13 2.02e+13 ...
## $ last_scraped : chr "2019-07-09" "2019-07-08" "2019-07-08" "2019-07-08" ...
## $ summary : chr "Newly renovated brick building with old world charm. Cozy room with private bathroom in third floor apartment." "This immaculate two bedroom one bath is conveniently located in the very desirable Ft. Green/ Clinton Hill sect"| __truncated__ "Spacious bedroom in a large well designed apartment. Two large windows and a large closet. Room is closest to t"| __truncated__ "Just steps from it all, this sunny south facing apt has everything you need for a comfortable stay in stylish W"| __truncated__ ...
## $ experiences_offered : chr "none" "none" "none" "none" ...
## $ host_id : int 113295877 213647282 42082221 34000566 347642 33518883 18950563 13878635 78414842 53413881 ...
## $ host_since : 'difftime' num 105 -483 628 719 ...
## ..- attr(*, "units")= chr "days"
## $ host_response_time : chr "within an hour" "within an hour" "within an hour" "within an hour" ...
## $ host_response_rate : num 1 0.99 0.99 0.99 1 0.99 1 1 0.99 0.99 ...
## $ host_acceptance_rate : chr "N/A" "N/A" "N/A" "N/A" ...
## $ host_is_superhost : Factor w/ 3 levels "","f","t": 2 2 2 2 2 2 2 2 2 2 ...
## $ host_listings_count : int 3 1 1 1 5 2 1 2 1 1 ...
## $ host_identity_verified : Factor w/ 3 levels "","f","t": 2 2 3 3 3 3 3 2 2 3 ...
## $ neighbourhood : chr "Staten Island" "Brooklyn" "Manhattan" "Williamsburg" ...
## $ neighbourhood_group_cleansed : chr "Staten Island" "Brooklyn" "Manhattan" "Brooklyn" ...
## $ city : chr "Staten Island" "Brooklyn" "New York" "Brooklyn" ...
## $ state : chr "NY" "NY" "NY" "NY" ...
## $ smart_location : chr "Staten Island, NY" "Brooklyn, NY" "New York, NY" "Brooklyn, NY" ...
## $ country_code : chr "US" "US" "US" "US" ...
## $ country : chr "United States" "United States" "United States" "United States" ...
## $ latitude : num 40.6 40.7 40.8 40.7 40.7 ...
## $ longitude : num -74.1 -74 -73.9 -74 -73.9 ...
## $ is_location_exact : Factor w/ 2 levels "f","t": 2 2 1 1 2 2 2 1 2 2 ...
## $ property_type : Factor w/ 36 levels "Aparthotel","Apartment",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ room_type : Factor w/ 3 levels "Entire home/apt",..: 2 1 2 1 1 2 1 2 2 2 ...
## $ accommodates : int 2 5 2 2 4 2 2 1 2 2 ...
## $ bathrooms : num 1 1 1 1 1 1 1 1 1 1 ...
## $ bedrooms : num 1 2 1 1 2 1 1 1 1 1 ...
## $ beds : num 1 4 1 1 3 1 1 1 1 0 ...
## $ bed_type : Factor w/ 5 levels "Airbed","Couch",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ price : num 55 200 60 95 100 90 75 90 200 55 ...
## $ guests_included : int 1 1 2 1 4 1 1 1 1 1 ...
## $ extra_people : num 5 0 50 0 30 0 30 0 0 15 ...
## $ minimum_nights : int 3 2 1 2 3 1 20 2 3 2 ...
## $ maximum_nights : int 1125 7 1125 1125 45 1125 1125 1125 1125 1125 ...
## $ minimum_minimum_nights : int 3 2 1 2 3 1 20 2 3 2 ...
## $ maximum_minimum_nights : int 3 2 1 2 3 1 20 2 3 2 ...
## $ minimum_maximum_nights : int 1125 7 1125 1125 45 1125 1125 1125 1125 1125 ...
## $ maximum_maximum_nights : int 1125 7 1125 1125 45 1125 1125 1125 1125 1125 ...
## $ minimum_nights_avg_ntm : num 3 2 1 2 3 1 20 2 3 2 ...
## $ maximum_nights_avg_ntm : num 1125 7 1125 1125 45 ...
## $ calendar_updated : Factor w/ 92 levels "1 week ago","10 months ago",..: 13 52 23 45 12 18 14 14 65 26 ...
## $ has_availability : chr "t" "t" "t" "t" ...
## $ availability_30 : int 4 0 0 0 3 0 0 3 0 0 ...
## $ availability_60 : int 34 0 0 0 3 0 2 33 0 0 ...
## $ availability_90 : int 64 0 0 0 7 0 10 63 0 0 ...
## $ availability_365 : int 154 0 0 0 220 0 100 338 0 0 ...
## $ number_of_reviews : int 6 13 3 0 120 15 2 12 9 4 ...
## $ number_of_reviews_ltm : int 6 13 0 0 30 0 1 2 3 4 ...
## $ instant_bookable : Factor w/ 2 levels "f","t": 2 1 1 1 2 2 1 2 1 1 ...
## $ is_business_travel_ready : chr "f" "f" "f" "f" ...
## $ cancellation_policy : Factor w/ 6 levels "flexible","moderate",..: 2 4 1 1 4 4 4 1 2 4 ...
## $ calculated_host_listings_count_entire_homes : int 0 1 0 1 2 0 1 0 0 0 ...
## $ calculated_host_listings_count_private_rooms: int 3 0 1 0 1 2 0 2 1 1 ...
## $ calculated_host_listings_count_shared_rooms : int 0 0 0 0 0 0 0 0 0 0 ...
## $ TV : num 0 1 1 1 1 1 0 1 1 0 ...
## $ Internet : num 0 0 1 1 1 1 1 0 0 0 ...
## $ AirCondition : num 1 1 1 1 1 1 1 0 1 1 ...
## $ Pets : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kitchen : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Breakfast : num 1 1 0 0 0 0 0 0 0 0 ...train$price=NULL
feature_names <- names(train)
test$price=NULL
dtrain <- xgb.DMatrix(as.matrix(sapply(train, as.numeric)),label=target_train, missing=NA)## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coerciondtest <- xgb.DMatrix(as.matrix(sapply(test, as.numeric)),label=target_test, missing=NA)## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercionSet up cross-validation scheme (3-fold)
foldsCV <- createFolds(target_train, k=7, list=TRUE, returnTrain=FALSE)
param <- list(booster = "gblinear"
, objective = "reg:linear"
, subsample = 0.7
, max_depth = 5
, colsample_bytree = 0.7
, eta = 0.037
, eval_metric = 'mae'
, base_score = 0.012 #average
, min_child_weight = 100)
xgb_cv <- xgb.cv(data=dtrain,
params=param,
nrounds=100,
prediction=TRUE,
maximize=FALSE,
folds=foldsCV,
early_stopping_rounds = 30,
print_every_n = 5)## [1] train-mae:73.672862+0.111883 test-mae:73.678424+0.805665
## Multiple eval metrics are present. Will use test_mae for early stopping.
## Will train until test_mae hasn't improved in 30 rounds.
##
## [6] train-mae:71.617013+0.196163 test-mae:71.626800+0.853066
## [11] train-mae:68.533311+0.181592 test-mae:68.547896+0.831818
## [16] train-mae:66.164162+0.178459 test-mae:66.182204+0.808597
## [21] train-mae:64.476784+0.174080 test-mae:64.500718+0.792143
## [26] train-mae:63.206394+0.177173 test-mae:63.231299+0.783250
## [31] train-mae:62.232872+0.175572 test-mae:62.258859+0.784180
## [36] train-mae:61.470202+0.175628 test-mae:61.496858+0.790255
## [41] train-mae:60.854143+0.172496 test-mae:60.880601+0.798381
## [46] train-mae:60.345085+0.176652 test-mae:60.372223+0.798627
## [51] train-mae:59.921587+0.177307 test-mae:59.950626+0.801231
## [56] train-mae:59.564381+0.175756 test-mae:59.594660+0.804870
## [61] train-mae:59.256600+0.175789 test-mae:59.287972+0.806301
## [66] train-mae:58.985555+0.174694 test-mae:59.018218+0.808247
## [71] train-mae:58.745945+0.174260 test-mae:58.779264+0.810722
## [76] train-mae:58.533288+0.174144 test-mae:58.566710+0.813143
## [81] train-mae:58.339680+0.173086 test-mae:58.373902+0.815930
## [86] train-mae:58.163766+0.172355 test-mae:58.199008+0.817836
## [91] train-mae:58.003126+0.172493 test-mae:58.039364+0.818647
## [96] train-mae:57.854376+0.171417 test-mae:57.891479+0.819875
## [100] train-mae:57.742967+0.171288 test-mae:57.780617+0.819684Check best results and get best nrounds
print(xgb_cv$evaluation_log[which.min(xgb_cv$evaluation_log$test_mae_mean)])## iter train_mae_mean train_mae_std test_mae_mean test_mae_std
## 1: 100 57.74297 0.1712877 57.78062 0.8196839nrounds <- xgb_cv$best_iteration
xgb <- xgb.train(params = param
, data = dtrain
# , watchlist = list(train = dtrain)
, nrounds = nrounds
, verbose = 1
, print_every_n = 5
#, feval = amm_mae
)Feature Importance
importance_matrix <- xgb.importance(feature_names,model=xgb)
xgb.plot.importance(importance_matrix[1:15,])
head(test)Predict
preds <- predict(xgb,dtest)
preds## [1] 88.880173 99.294037 144.362793 117.910576 85.451347
## [6] 97.283577 219.103470 77.462975 163.462601 159.818100
## [11] 169.157852 416.755280 126.675903 203.280579 142.627441
## [16] 250.977692 152.276611 219.252670 167.497559 314.249878
## [21] 189.745117 191.274658 147.411789 94.378220 99.858559
## [26] 141.798981 150.004089 74.939888 293.959167 131.730469
## [31] 175.831131 126.955063 112.523193 47.001705 91.929718
## [36] 143.569275 158.944931 256.066223 79.791061 94.278687
## [41] 117.194641 130.682587 82.783455 197.966446 187.734222
## [46] 87.860672 80.071960 152.438065 162.253433 185.950348
## [51] 125.543045 98.419861 133.362137 176.997437 178.821976
## [56] 110.297058 221.662643 127.133934 161.428650 131.290619
## [61] 69.073967 176.573639 153.242828 103.154778 95.349892
## [66] 170.783585 80.206551 122.467125 76.180305 145.304626
## [71] 142.322266 78.873466 108.047173 121.531326 86.681221
## [76] 168.126221 195.360031 50.426228 270.346802 62.448090
## [81] 138.070068 184.043350 139.737122 82.550743 157.040588
## [86] 134.906006 104.806168 93.089058 113.384018 72.620003
## [91] 79.966545 90.062119 206.938324 70.459534 103.165649
## [96] 119.265434 134.291626 94.136917 108.229660 127.046341
## [101] 86.306793 129.043198 167.381348 68.831764 79.453056
## [106] 72.718208 146.962463 93.334297 149.676331 139.543106
## [111] 155.459305 102.892136 175.964355 150.554413 104.050072
## [116] 118.834366 137.258987 107.832603 76.040321 128.698593
## [121] 127.007019 82.340477 259.747955 92.751595 136.392838
## [126] 238.168823 125.119316 77.584740 115.657845 80.093697
## [131] 86.628738 56.567238 129.805191 161.675476 107.924248
## [136] 85.821152 128.810684 79.589272 116.724289 101.043015
## [141] 63.461861 135.744095 228.609436 115.947762 181.738739
## [146] 62.741898 88.714157 238.467438 112.320190 127.840126
## [151] 141.322800 69.163437 122.844009 146.221756 76.232269
## [156] 146.212830 328.082520 137.772659 131.789368 123.303543
## [161] 103.356064 202.647369 111.457825 89.852554 141.763275
## [166] 109.407860 164.635239 119.612343 113.276833 124.300781
## [171] 166.098969 106.900345 208.474197 133.856598 245.171738
## [176] 97.068756 286.366394 89.501579 134.775162 108.643883
## [181] 104.438141 189.853073 130.557114 98.555130 278.044006
## [186] 74.124847 82.521721 116.186806 148.699615 150.936035
## [191] 129.187180 139.460785 96.058685 116.602783 245.479187
## [196] 91.657227 78.637245 394.926666 157.972122 175.508469
## [201] 102.224632 140.708374 210.728806 185.791855 191.582809
## [206] 264.669891 281.234955 314.304871 201.978653 209.908875
## [211] 144.289581 166.296417 102.216171 112.229683 105.092522
## [216] 121.114990 233.957123 121.993675 175.158310 100.923233
## [221] 105.068695 88.035851 122.417534 86.670357 106.604218
## [226] 113.199875 140.224564 173.132584 98.660667 77.997093
## [231] 186.424927 -26.158016 96.932190 96.270409 205.965927
## [236] 101.289452 -26.916622 -18.400436 161.874237 162.949829
## [241] 69.052811 112.296577 275.569427 322.637054 119.295074
## [246] 124.612755 126.437447 216.252548 532.605347 152.624481
## [251] 135.467255 249.222626 152.866867 107.256325 49.825199
## [256] 85.618683 60.907810 89.945633 90.644905 151.878235
## [261] 274.741547 78.509636 148.606888 139.121246 108.925316
## [266] 136.071518 119.371574 244.123550 124.460541 134.768814
## [271] 72.725136 82.106262 144.193680 144.210785 116.096161
## [276] 223.606308 228.135544 93.412964 55.853092 86.304504
## [281] 171.455887 186.317017 225.808624 130.199081 196.228638
## [286] 95.929459 168.943237 158.669220 118.337799 95.945274
## [291] 130.171890 93.103073 350.211639 175.767075 102.934288
## [296] 198.338684 95.281761 142.867462 53.452564 153.710648
## [301] 187.131042 218.766129 90.125145 120.339951 147.475601
## [306] 153.712311 222.972366 129.414978 127.928772 79.718666
## [311] 214.644135 183.525192 117.842201 229.033600 103.434738
## [316] 268.917633 91.764664 76.130859 125.786224 165.318176
## [321] 114.809723 112.137825 177.021393 86.699860 134.851990
## [326] 215.999466 156.135925 75.834251 79.948204 81.415497
## [331] 83.066635 123.663033 257.530060 140.946106 146.703705
## [336] 111.089859 243.571030 80.150948 114.139893 116.740860
## [341] 154.745087 191.168793 505.400909 228.535675 188.646652
## [346] 137.274704 132.709824 90.442101 139.363388 117.081894
## [351] 146.176224 91.781303 124.746078 63.457256 285.237000
## [356] 117.758263 115.900452 92.043571 103.605942 111.839439
## [361] 240.076035 231.977036 111.112007 99.690331 129.342422
## [366] 67.026558 65.908043 211.236298 88.242249 104.935333
## [371] 259.144104 73.762810 210.073257 196.429398 282.528656
## [376] 111.366821 124.906296 139.240311 101.631264 108.422127
## [381] 131.176208 97.567879 48.536404 104.368507 106.569633
## [386] 89.984520 117.685211 242.711441 252.618179 122.683960
## [391] 104.140472 109.727852 304.216583 92.967598 86.359329
## [396] 289.156158 81.349319 79.193993 131.358917 132.562286
## [401] 82.373299 104.863976 161.994522 98.862846 81.815323
## [406] 118.928421 157.762466 212.226791 92.590546 105.256149
## [411] 206.820480 89.902733 97.049469 149.433609 104.890602
## [416] 100.418724 175.636414 85.100662 119.220970 153.985107
## [421] 92.388168 221.401978 134.495361 94.740768 110.789978
## [426] 319.077911 139.008530 131.571213 157.898178 130.680878
## [431] 162.772400 87.887291 101.271233 161.122726 205.366486
## [436] 156.153641 88.978485 140.182800 143.890686 172.896362
## [441] 103.814964 176.208130 174.173172 82.135414 176.072128
## [446] 91.511200 72.653259 140.413132 164.445999 166.926422
## [451] 83.636589 289.235077 192.916824 63.447708 156.014832
## [456] 151.170883 318.497864 108.164810 78.145805 128.793488
## [461] 85.436951 176.279800 109.969307 112.320755 94.622475
## [466] 257.013123 138.056381 77.007492 104.938759 113.061234
## [471] 126.243111 251.849167 103.950546 153.699265 97.442657
## [476] 108.857925 210.745743 156.867294 95.489029 141.524429
## [481] 108.652473 193.493378 243.546783 80.876198 143.049103
## [486] 125.900070 164.093307 117.118042 193.443420 225.717743
## [491] 92.701439 120.109230 125.822372 108.791039 92.557648
## [496] 98.195190 142.477921 112.036568 107.318329 171.583694
## [501] 218.633087 176.085846 191.226501 115.589813 134.612915
## [506] 131.637833 114.034462 91.300148 102.438370 80.704773
## [511] 130.013779 98.640869 140.354919 396.503632 161.256973
## [516] 108.132805 92.535660 87.775810 101.684021 84.295784
## [521] 181.059647 155.007645 158.328705 93.556602 100.527611
## [526] 100.373619 56.551857 133.125809 187.523590 92.691673
## [531] 318.986816 117.130333 116.514206 146.301544 90.406281
## [536] 343.949249 154.828156 73.094086 91.550018 213.225174
## [541] 126.898270 157.964844 111.233322 253.486176 112.540611
## [546] 83.315277 68.864136 169.579102 95.841858 135.618042
## [551] 83.907692 108.744713 121.846802 85.665405 154.449997
## [556] 233.836121 92.323982 331.429871 294.065155 173.813583
## [561] 166.704193 125.089218 142.580795 82.534561 137.784546
## [566] 104.040207 72.220390 121.409332 84.333557 196.224442
## [571] 158.581299 138.598785 90.401100 103.522316 84.107666
## [576] 139.225174 83.179497 -7.446103 102.100433 140.630280
## [581] 129.831024 102.478333 150.088104 -25.957058 136.919174
## [586] 188.128891 67.602715 194.475388 134.793640 201.616440
## [591] 100.200531 141.606277 81.994385 76.956169 160.615753
## [596] 108.332436 168.247971 105.180031 154.595367 131.245178
## [601] 104.211403 124.426231 99.906044 270.411041 73.699203
## [606] 148.336166 106.124893 120.591057 119.241020 111.631943
## [611] 197.797424 133.142990 72.608696 58.973881 164.623093
## [616] 120.881454 217.360626 107.730881 187.705017 113.262466
## [621] 103.900146 169.497269 100.102829 307.928406 83.129051
## [626] 90.767509 85.643105 110.290047 77.580254 128.338013
## [631] 106.993423 71.000526 119.820488 87.798325 103.730583
## [636] 90.766960 113.507187 128.332840 141.496445 103.730072
## [641] 170.806473 194.863937 293.350861 242.600433 100.801918
## [646] 94.852310 115.289207 132.582825 88.866669 142.590866
## [651] 152.893799 160.302383 165.444901 89.686005 143.954086
## [656] 210.235413 85.698410 217.345474 106.194084 -13.478653
## [661] 241.587662 100.422211 204.364532 69.639137 193.152634
## [666] 146.767197 201.577332 128.217270 128.101410 67.998421
## [671] 109.920181 82.940475 155.970718 96.550079 168.322540
## [676] 166.595901 87.295570 158.843887 86.200401 253.147263
## [681] 82.548653 62.883087 71.633812 67.067337 190.356918
## [686] 165.506119 116.657639 311.911133 88.012245 75.194893
## [691] 104.314293 143.210892 253.065811 99.591011 99.880547
## [696] 217.425507 88.959808 193.376419 80.635735 163.574203
## [701] 134.865997 304.360046 135.256317 76.548943 268.987640
## [706] 82.361526 145.021393 120.412209 100.004364 210.468399
## [711] 198.246613 92.324837 207.559952 172.510117 219.589172
## [716] 78.652184 96.996521 129.443039 81.253944 185.382690
## [721] 113.207687 111.951157 97.919678 96.204323 207.384277
## [726] 116.552254 139.253754 176.122681 149.088821 86.445900
## [731] 124.497047 99.489723 133.467087 90.911003 102.229988
## [736] 112.939514 252.694443 191.705261 112.802109 89.983017
## [741] 247.403549 133.483673 90.850891 135.493744 65.332031
## [746] 407.406372 103.956940 86.916603 162.920776 100.891068
## [751] 165.435059 126.422234 160.098007 206.372162 41.062298
## [756] 75.540947 222.513901 46.821636 103.432846 83.898109
## [761] 201.826630 73.515579 106.820694 111.103653 217.012070
## [766] 86.443596 161.170166 154.047165 350.973145 128.851196
## [771] 136.101654 194.595703 75.818565 74.893349 200.386063
## [776] 134.458588 113.974480 309.637329 170.622177 74.875801
## [781] 73.086250 -22.262100 100.074532 155.020264 157.922989
## [786] 83.245560 85.452446 134.229858 90.670624 31.611259
## [791] 140.783310 160.375443 89.759048 104.556709 88.063202
## [796] 121.674454 206.485886 103.012115 46.299995 110.644447
## [801] 283.709747 165.541183 141.005814 147.200378 118.498764
## [806] 84.268471 147.523529 105.630447 140.658859 116.048180
## [811] 143.405380 88.288940 175.614319 99.414009 132.435944
## [816] 184.169617 186.555710 98.560806 131.762970 205.517944
## [821] 82.251595 172.717529 142.625839 78.026085 311.858948
## [826] 123.027672 190.376846 100.274536 63.523708 190.464081
## [831] 114.286362 193.270737 69.016106 120.193672 155.522766
## [836] 85.181175 83.810432 218.670258 337.082153 67.716347
## [841] 399.641663 87.928780 173.711487 110.278976 95.653046
## [846] 86.244865 138.936981 62.899437 160.975708 108.504166
## [851] 147.921463 205.485840 305.044708 332.525055 240.748901
## [856] 209.913620 126.963875 126.633904 245.628738 127.302879
## [861] 99.332558 87.303040 124.908684 150.230850 148.650604
## [866] 95.391937 89.299324 80.809891 117.053391 90.285034
## [871] 150.653931 202.910858 136.078171 247.380783 117.643799
## [876] 85.316963 112.598312 118.500008 188.598389 186.766693
## [881] 83.721451 81.558006 91.017281 94.414665 83.342690
## [886] 145.848068 71.217743 181.374786 59.330769 322.877228
## [891] 96.269051 140.508194 140.577026 180.684891 165.387100
## [896] 106.515228 69.204720 226.787018 132.439667 71.475395
## [901] 85.837006 129.527603 111.317734 109.097809 131.012802
## [906] 218.102707 165.409851 109.021065 307.250000 89.005386
## [911] 134.321548 72.238564 81.364433 102.312767 82.162445
## [916] -20.404491 202.628021 96.420845 73.777695 74.576172
## [921] 78.816628 114.492081 243.581589 101.592148 157.090698
## [926] 115.193642 152.256409 61.536156 142.934982 179.859512
## [931] 107.763870 216.773788 102.698868 201.910568 113.347656
## [936] 88.779877 85.846176 117.987289 100.877625 146.052765
## [941] 151.595108 132.911194 169.970490 119.455261 69.266670
## [946] 228.942535 149.256592 107.796249 157.717087 118.321442
## [951] 72.808708 187.056274 119.095306 129.740326 121.657516
## [956] 125.518341 224.239807 121.858986 61.856380 238.507812
## [961] 117.128883 114.608315 145.076767 118.360970 124.237320
## [966] 82.301208 121.095505 76.062561 142.580215 163.877014
## [971] 253.354797 168.234665 244.172531 199.220871 190.808487
## [976] 91.734337 314.280212 196.610794 92.090797 85.212242
## [981] 189.458450 74.070213 28.477516 112.599869 100.417091
## [986] 151.258026 114.975357 155.061111 165.094040 221.840668
## [991] 132.320831 116.934128 80.382065 195.470306 137.334122
## [996] 227.376999 325.103058 99.612762 122.651512 553.232056
## [1001] 119.542984 108.378372 106.145821 144.068512 116.799805
## [1006] 166.833527 106.747231 103.634293 431.922729 122.593163
## [1011] 88.870316 157.487305 118.822678 106.770889 210.137589
## [1016] 105.332718 148.738159 95.648132 145.793640 197.668243
## [1021] 77.363472 118.029236 110.887787 89.990250 122.175049
## [1026] 209.233170 140.063278 31.347530 223.033875 112.254120
## [1031] 236.330460 110.113388 122.091385 324.407074 287.910828
## [1036] 217.212372 96.218735 142.016220 169.750626 130.016098
## [1041] 87.894516 152.329407 193.228912 190.484039 212.776489
## [1046] 156.675858 194.243881 196.496094 210.195969 91.322731
## [1051] 152.699203 215.825424 359.705627 274.614105 221.911682
## [1056] 86.143501 143.878754 91.994713 148.982986 144.162872
## [1061] 119.243546 73.362221 115.111832 200.828751 200.512955
## [1066] 105.926178 128.568176 210.054321 87.588623 171.612106
## [1071] 213.871094 132.440186 89.513046 112.152641 83.422783
## [1076] 55.984093 130.966904 112.116814 132.913315 108.434875
## [1081] 72.003662 99.810059 102.744919 134.618668 102.260704
## [1086] 111.839569 278.879913 137.544678 70.140602 123.111519
## [1091] 140.124557 56.395218 118.662292 127.365852 79.127739
## [1096] 103.133026 149.795242 130.800461 133.753052 89.152275
## [1101] 192.421921 167.417496 80.843605 49.318329 159.443268
## [1106] 219.016876 209.068970 154.723129 125.377106 111.253914
## [1111] 132.750916 196.712952 249.909393 99.120377 123.435776
## [1116] 219.602905 163.225586 149.542618 109.014023 141.546066
## [1121] 171.372406 113.322624 104.304451 85.738922 105.749504
## [1126] 93.909142 231.891449 107.083664 137.792191 340.467560
## [1131] 122.043137 130.851196 111.039131 142.935913 98.475754
## [1136] 280.364014 103.177956 136.367188 290.523193 175.280075
## [1141] 114.903908 150.043655 81.452118 83.188896 188.148941
## [1146] 103.361572 123.035912 251.731354 68.509605 63.501171
## [1151] 124.328377 205.827362 120.796982 120.062515 150.530853
## [1156] 109.251915 129.731812 126.901680 110.766159 102.170013
## [1161] 116.578217 225.406158 216.426056 106.639046 263.457916
## [1166] 90.803741 91.719482 50.489586 142.483521 69.450180
## [1171] 125.657959 62.784832 130.389236 85.688919 85.353920
## [1176] 177.510803 90.987610 129.266174 127.643646 66.770187
## [1181] 83.518219 131.460556 253.970383 87.374321 102.041992
## [1186] 57.457199 105.040405 100.125786 100.344856 129.225784
## [1191] 118.488907 107.673897 99.530037 100.178261 73.093422
## [1196] 87.426109 94.915871 86.084229 110.128288 105.452621
## [1201] 112.886871 170.248276 117.703789 98.001839 119.195724
## [1206] 138.856506 72.963120 109.490967 176.081787 87.602112
## [1211] 91.852089 94.729767 118.213989 128.380264 83.570351
## [1216] 243.665817 97.050926 96.021492 70.331207 87.950737
## [1221] 89.170006 115.339020 207.350327 102.786331 111.997940
## [1226] 238.566772 105.324562 120.676201 148.057663 75.766655
## [1231] 128.240952 117.875000 147.607162 295.472382 145.815552
## [1236] 88.346184 99.679459 86.564865 110.401283 95.386101
## [1241] 90.131592 509.197296 136.317963 115.342857 98.603119
## [1246] 104.620789 117.127213 153.081055 90.886589 131.440643
## [1251] 109.914139 264.873627 160.576736 73.187248 90.181000
## [1256] 114.028389 92.771072 70.160362 107.808159 258.851288
## [1261] 66.567719 101.653633 107.948524 107.181374 84.715828
## [1266] 184.262268 68.218346 147.511597 79.198593 101.812584
## [1271] 93.668633 215.366287 117.851807 212.702393 116.441437
## [1276] 82.549080 99.927338 146.480270 204.180618 107.484306
## [1281] 126.756508 148.837097 111.084984 109.764610 107.572647
## [1286] 102.026199 50.785141 90.888107 113.667641 110.891724
## [1291] 104.367485 273.807098 154.654419 256.779999 114.383560
## [1296] 175.889572 101.094505 151.738510 97.988724 84.822128
## [1301] 136.729401 163.625504 124.916748 183.845337 90.831970
## [1306] 78.879196 147.579666 121.960152 106.042938 81.422684
## [1311] 215.233383 118.483421 77.140625 208.642868 93.571457
## [1316] 112.058563 137.858414 148.121490 115.106415 103.870934
## [1321] 92.522972 109.494751 117.300819 113.168610 91.160683
## [1326] 136.276230 116.012260 94.909637 118.449593 92.707451
## [1331] 88.812851 203.260040 185.509933 126.195717 92.921249
## [1336] 88.452065 139.886520 112.643692 109.916115 105.213219
## [1341] 111.792618 114.048683 70.763557 109.724640 91.566719
## [1346] 144.902695 131.989182 164.995651 122.366447 78.986481
## [1351] 192.163925 103.390656 106.569687 131.157333 178.318985
## [1356] 109.721581 253.297073 104.509239 275.714874 116.948708
## [1361] 102.674683 83.064873 129.460693 186.284317 158.930481
## [1366] 173.489319 195.865234 191.581421 179.617264 76.160629
## [1371] 117.928017 122.557808 104.070297 90.752350 109.723953
## [1376] 87.943748 153.823624 98.517021 113.023781 130.197128
## [1381] 117.538002 185.476334 190.043320 62.505005 91.754364
## [1386] 116.084290 65.240089 316.953796 191.032608 77.871368
## [1391] 172.016663 54.632103 95.052071 101.847237 136.841370
## [1396] 115.479980 108.073746 170.961151 192.760773 155.091751
## [1401] 87.864784 131.466232 129.525482 89.880676 68.870476
## [1406] 169.000702 81.909164 114.280304 83.486954 224.309082
## [1411] 189.908646 148.443726 237.803665 88.495636 136.045319
## [1416] 127.368744 305.707947 178.272369 145.381714 141.527130
## [1421] 172.299820 74.463150 103.543457 68.044502 128.718216
## [1426] 94.035606 143.811172 89.990623 257.033752 77.783348
## [1431] 68.612686 70.109818 66.828255 262.690521 60.056240
## [1436] 107.078377 82.948738 316.889130 231.120178 136.213486
## [1441] 104.208740 92.565567 93.684570 133.174545 94.159081
## [1446] 107.681152 86.734238 123.500824 34.061768 72.214737
## [1451] 149.400925 99.418167 175.728821 116.654205 79.719597
## [1456] 163.942062 145.899994 278.359589 85.485611 152.851700
## [1461] 147.284546 97.681084 118.527313 92.797195 58.580864
## [1466] 108.732590 184.226364 107.733734 237.641800 91.639313
## [1471] 91.969803 66.372833 229.827408 122.986992 115.560638
## [1476] 117.632156 156.347702 148.258865 129.056030 155.336243
## [1481] 93.236908 152.540344 102.261215 121.816505 118.114220
## [1486] 51.056309 86.643188 153.579758 136.918457 159.444366
## [1491] 141.989349 67.647865 80.069237 63.477604 207.121048
## [1496] 85.716202 132.693085 77.424515 95.449562 108.401596
## [1501] 84.502357 76.417953 130.910568 82.685829 148.155548
## [1506] 235.246597 125.824249 56.611649 101.003494 109.894897
## [1511] 149.220566 87.847763 132.363403 101.591583 112.133400
## [1516] 164.889786 164.078735 89.697357 188.906189 104.068047
## [1521] 109.469887 81.391724 133.438828 91.778633 70.246094
## [1526] 101.625397 164.792145 169.377731 131.565704 96.445831
## [1531] 169.786713 276.673798 123.604439 78.129868 132.694305
## [1536] 84.233253 82.293556 86.076836 176.576508 122.427948
## [1541] 103.313568 100.857864 80.333809 104.100754 196.367264
## [1546] 87.636475 207.210587 113.265633 89.481750 96.960434
## [1551] 63.864822 82.016579 67.446922 139.440079 68.031952
## [1556] 119.909592 99.319275 161.453201 111.166283 161.722137
## [1561] 66.932526 79.786354 164.764755 120.459915 165.449768
## [1566] 172.560242 273.550293 191.547775 83.808647 133.182465
## [1571] 116.158669 88.660591 358.152863 84.126808 264.092133
## [1576] 217.893372 86.457489 179.176132 90.516365 144.360077
## [1581] 184.379379 136.628464 109.033127 98.957672 77.127739
## [1586] 71.814201 111.687119 106.640015 94.799591 97.305214
## [1591] 91.990990 114.277588 175.090576 144.274811 134.466705
## [1596] 84.861954 148.952087 125.264023 71.770966 158.899139
## [1601] 97.539848 73.439873 108.316147 122.386543 426.445801
## [1606] 94.307640 180.171631 158.333527 86.070976 113.289429
## [1611] 104.318474 169.993027 81.039009 332.499786 143.771713
## [1616] 65.598320 102.574661 103.403046 183.259186 120.617409
## [1621] 183.842102 85.837982 88.697838 139.297806 128.783203
## [1626] 145.575211 160.039719 141.008865 119.677872 87.803322
## [1631] 99.511932 141.764465 62.053020 421.910645 162.814041
## [1636] 283.579163 265.942078 235.903534 103.063156 118.794876
## [1641] 99.710594 71.461624 202.556030 146.314011 92.000504
## [1646] 101.954254 90.747772 168.664337 281.537598 142.610321
## [1651] 176.494034 160.418045 132.971802 145.734924 105.467773
## [1656] 144.367676 196.544220 179.632492 176.609512 153.505478
## [1661] 73.599289 180.659851 122.702484 194.092834 142.463089
## [1666] 123.730591 245.847137 103.011711 187.186081 181.428772
## [1671] 179.664246 191.864655 160.625534 66.280449 79.642586
## [1676] 120.090355 129.135391 133.953278 104.481934 129.535965
## [1681] 129.166733 131.229660 124.522980 129.694946 99.361526
## [1686] 191.086746 236.939713 169.137878 186.396591 144.515564
## [1691] 104.487442 115.419899 100.303337 263.054504 192.500778
## [1696] 127.264313 181.274719 124.062057 267.461914 240.470810
## [1701] 128.130981 149.096817 65.156639 84.857498 203.307785
## [1706] 182.275986 103.279587 95.457916 138.222427 73.245094
## [1711] 118.508331 223.154709 199.766296 97.650360 123.469536
## [1716] 70.876953 152.431580 158.636597 167.632187 119.047195
## [1721] 73.462502 202.619797 164.688477 55.766495 149.471176
## [1726] 138.725708 53.128681 207.106125 181.395493 257.873260
## [1731] 239.919861 185.532166 93.761269 303.256714 160.808258
## [1736] 246.692963 206.257706 158.657440 100.927322 299.548096
## [1741] 190.490402 112.524986 121.779419 283.156250 209.189041
## [1746] 157.838303 91.656364 132.116852 44.319118 245.157593
## [1751] 106.817825 143.297104 264.227112 113.725182 279.419373
## [1756] 173.156052 124.357758 88.173294 66.706123 150.311295
## [1761] 107.828781 146.610092 109.050583 120.266449 80.793556
## [1766] 122.961288 150.709213 151.801361 203.941376 58.983685
## [1771] 97.350250 138.064117 123.455162 122.414444 204.101730
## [1776] 233.975861 139.544418 118.109474 160.605087 103.313850
## [1781] 185.732315 149.992065 144.351364 72.738861 117.250183
## [1786] 87.861038 84.701492 125.339348 129.577194 77.539375
## [1791] 177.577072 169.700882 33.838093 155.675262 178.602310
## [1796] 120.804810 241.130402 117.518677 109.373260 312.087677
## [1801] 121.056664 101.933876 137.609222 182.717819 94.071907
## [1806] 258.643890 120.365486 214.523300 258.356934 154.242188
## [1811] 154.815033 297.514587 79.998337 259.588165 101.032104
## [1816] 131.648834 104.302887 144.268753 248.753113 100.822556
## [1821] 269.799500 130.616058 163.094452 138.575150 112.237274
## [1826] 133.491592 248.384064 152.733444 89.012199 533.791748
## [1831] 89.975777 105.323921 169.242767 136.388458 148.480301
## [1836] 234.686935 73.154205 85.119499 129.774170 145.655029
## [1841] 151.412582 181.250198 169.169586 139.675293 123.903336
## [1846] 89.908592 110.128830 129.343475 203.167419 298.713470
## [1851] 120.356033 102.144249 204.619186 148.616821 125.555122
## [1856] 132.642456 101.056686 146.901474 121.428314 142.879791
## [1861] 117.784355 134.754745 276.174652 172.004318 226.815491
## [1866] 116.744003 154.329926 99.783691 122.851822 92.476112
## [1871] 124.584015 289.557739 148.485764 110.348923 385.349243
## [1876] 120.494537 138.515991 156.127060 174.102676 171.436630
## [1881] 351.282471 173.948578 102.037292 152.289597 173.286209
## [1886] 124.826263 114.654022 67.732559 100.937164 302.845612
## [1891] 136.964584 255.179581 106.138580 205.446548 191.986176
## [1896] 135.926254 257.973114 86.861588 123.874344 191.516403
## [1901] 106.527328 277.409180 155.164062 121.295334 148.398666
## [1906] 113.261887 136.250641 104.502724 189.581314 265.849854
## [1911] 88.360649 142.389893 126.364586 202.023514 120.735809
## [1916] 216.104218 165.779373 117.815979 107.976303 158.098343
## [1921] 156.729477 153.521408 121.545425 247.602951 75.525108
## [1926] 126.582779 254.483231 86.341507 163.822556 121.010620
## [1931] 173.688843 174.827621 110.917923 158.328934 116.123566
## [1936] 148.525787 68.974953 136.673203 280.916260 173.019592
## [1941] 201.663879 98.971954 106.857391 128.072891 131.444809
## [1946] 229.963181 137.885773 126.911934 179.641708 421.864563
## [1951] 90.232155 119.216934 207.770966 98.174309 137.517975
## [1956] 159.009460 119.378723 132.883942 164.556061 91.455925
## [1961] 278.431793 72.438271 131.161621 122.818703 141.750183
## [1966] 73.224403 121.563477 65.881187 190.428528 101.528969
## [1971] 82.713165 71.473129 131.655838 157.107635 92.441139
## [1976] 106.087593 87.179939 128.844040 96.653313 164.760208
## [1981] 257.134521 147.208618 81.856415 166.105850 94.557457
## [1986] 106.273186 117.119339 135.290176 80.882378 112.484596
## [1991] 151.121552 184.620331 136.375214 83.814095 174.450287
## [1996] 93.787003 96.184074 151.917694 258.360016 200.384598
## [2001] 76.839569 112.963623 136.598709 96.371887 167.444946
## [2006] 236.927017 133.382538 97.365814 111.237350 65.783119
## [2011] 186.329285 46.939510 134.923248 175.231415 124.190430
## [2016] 114.759888 131.963623 84.288200 99.447144 220.013367
## [2021] 77.470978 276.062836 114.177269 122.265671 181.817902
## [2026] 67.821793 92.761086 142.152039 193.330536 64.863068
## [2031] 133.564911 148.379654 134.054733 357.552582 255.326767
## [2036] 125.926056 204.021057 108.340088 234.538467 133.555161
## [2041] 95.896675 422.037476 101.235092 193.706955 104.194656
## [2046] 162.997940 108.070198 126.246201 316.257294 150.017334
## [2051] 63.165699 84.410851 171.117508 103.228973 184.181961
## [2056] 128.134460 96.351128 153.885193 137.993439 146.109863
## [2061] 55.486549 125.815994 74.643288 147.303848 209.121445
## [2066] 81.348869 138.495544 78.516380 138.899933 105.118408
## [2071] 256.828674 279.139435 71.244278 228.747498 112.244621
## [2076] 283.406708 147.858551 140.016693 100.916626 125.007950
## [2081] 292.258514 278.151642 143.912643 108.032700 108.535538
## [2086] 123.837746 116.700867 238.449173 155.430054 105.810295
## [2091] 165.993866 281.593628 232.270432 110.088608 91.897171
## [2096] 158.958511 147.643188 103.893539 139.255219 113.384315
## [2101] 126.984116 213.673904 170.239197 85.942070 122.733002
## [2106] 102.657784 251.346359 123.444542 118.229958 72.907974
## [2111] 128.323547 113.339691 103.196259 109.709450 171.197403
## [2116] 124.942993 137.995941 105.786613 118.212181 148.905334
## [2121] 124.760994 73.690575 116.196350 327.708923 179.887009
## [2126] 88.248283 82.817131 116.947708 315.333374 162.986237
## [2131] 119.654228 132.059265 114.155785 112.629166 92.471481
## [2136] 220.177505 191.877686 60.312283 78.799004 90.413193
## [2141] 114.488815 103.667786 36.240917 90.480690 78.669792
## [2146] 292.218018 200.711823 221.217072 201.059113 96.483818
## [2151] 91.561104 155.796478 108.658386 108.437027 98.207039
## [2156] 119.237297 172.424332 66.292198 172.751770 282.699738
## [2161] 147.467819 116.623009 334.231995 101.492592 133.259949
## [2166] 244.259415 124.558434 231.003250 111.362289 115.507416
## [2171] 194.192764 364.636108 187.790359 139.696686 118.808777
## [2176] 176.567154 165.924362 390.580078 125.434814 93.851715
## [2181] 168.147705 210.974960 77.707840 133.668457 104.296913
## [2186] 106.751923 151.348267 129.706680 89.817474 202.416077
## [2191] 129.043365 235.467728 148.982391 102.855003 195.897964
## [2196] 260.558685 191.814301 138.430603 85.668823 61.956127
## [2201] 132.404816 79.069611 102.059235 115.448273 90.864845
## [2206] 189.150879 201.615005 121.420784 109.785637 90.712616
## [2211] 99.206207 273.273132 137.146500 99.158981 93.606529
## [2216] 101.330978 118.822014 134.056595 163.617538 141.714447
## [2221] 141.154007 69.205971 131.192047 31.014652 154.233978
## [2226] 62.576096 111.740768 146.716965 84.542900 138.840668
## [2231] 99.464256 58.804222 43.015179 36.262211 64.302147
## [2236] 87.698532 99.514069 291.302643 131.508133 84.584099
## [2241] 107.785492 164.945465 92.672455 138.595306 98.471817
## [2246] 284.216553 95.737640 183.758575 232.720627 103.058388
## [2251] 56.449799 116.444771 96.927864 185.686829 109.241592
## [2256] 303.857330 299.954224 110.109703 270.912109 66.355591
## [2261] 125.290802 181.182266 152.557083 118.120361 103.594551
## [2266] 92.628609 70.344254 100.802292 86.594727 126.110840
## [2271] 58.312599 77.184982 59.763603 159.057724 147.370590
## [2276] 89.577133 126.542442 178.070496 85.673790 220.014099
## [2281] 196.252533 82.051857 149.017838 115.141289 116.722633
## [2286] 142.484055 100.396797 70.972588 99.305145 129.529373
## [2291] 82.057213 185.376083 81.038658 79.183128 130.900406
## [2296] 110.827797 303.985168 140.372665 85.325607 96.709984
## [2301] 85.171898 151.502808 96.252785 156.798508 199.883743
## [2306] 316.477905 35.666481 656.172668 129.124893 85.326187
## [2311] 81.950264 134.271652 269.134460 277.843781 227.827759
## [2316] 72.145889 84.852272 163.091232 127.847374 305.606995
## [2321] 360.376434 145.294403 125.399925 118.414551 142.203262
## [2326] 89.166389 281.005035 121.008743 102.371994 75.246475
## [2331] 145.324112 208.966721 90.470001 60.053059 233.407928
## [2336] 128.494003 135.533356 142.136444 325.604980 105.040260
## [2341] 93.131386 92.603500 154.251434 75.992012 138.465576
## [2346] 147.399017 92.453079 117.048172 105.150513 196.957397
## [2351] 99.274612 77.547707 108.990616 109.493240 106.365578
## [2356] 156.259659 100.762810 76.749214 251.804520 104.001465
## [2361] 171.070892 68.763344 123.484856 76.919601 85.959175
## [2366] 173.130936 277.056488 105.780907 90.226868 152.404373
## [2371] 135.752731 230.490112 167.414978 137.627304 63.785519
## [2376] 269.350250 145.152756 217.439224 169.399323 77.872322
## [2381] 117.006165 60.149937 183.648193 95.736641 134.049667
## [2386] 194.680084 220.936600 244.893402 126.280724 258.710968
## [2391] 193.287521 183.596191 133.437546 72.033203 200.914185
## [2396] 149.113708 137.115204 202.665176 118.504883 140.644348
## [2401] 80.780785 210.920212 178.994354 83.548233 295.769379
## [2406] 106.295540 110.774094 685.056824 211.846375 114.980270
## [2411] 82.465561 104.572197 143.503799 126.431412 87.271660
## [2416] 244.034592 129.069183 73.470741 111.622864 275.993164
## [2421] 141.644409 274.833771 58.540264 71.094612 122.566109
## [2426] 68.170311 686.721008 156.546646 95.191757 121.479454
## [2431] 74.513817 92.251564 137.298508 179.082062 183.814301
## [2436] 72.109489 119.495239 119.510338 133.445541 220.648651
## [2441] 107.057602 241.471222 313.870667 358.792542 296.732147
## [2446] 73.803436 143.446426 74.883415 175.849197 133.484314
## [2451] 51.540531 114.427261 166.363068 375.641022 96.611221
## [2456] 79.196342 110.514915 104.063240 152.881958 108.632675
## [2461] 86.315117 47.869419 92.707001 96.171257 105.334198
## [2466] 114.064339 337.079468 99.162476 210.245636 157.666672
## [2471] 163.323669 152.627274 104.803741 67.436546 283.853882
## [2476] 182.855911 150.560562 248.217819 131.176422 124.102936
## [2481] 101.713959 90.571396 110.790237 104.229332 109.341393
## [2486] 74.522881 111.983215 198.618881 104.225235 230.537415
## [2491] 86.761917 135.491318 204.577942 92.611923 116.438904
## [2496] 188.929352 79.541885 257.301636 91.245270 85.422188
## [2501] 226.710922 97.496330 112.407127 360.940552 223.929611
## [2506] 50.919193 92.362686 119.350670 -6.759485 231.317047
## [2511] 167.316467 73.790359 105.168961 82.537323 134.756027
## [2516] 132.616135 165.330719 121.524879 110.853020 96.863792
## [2521] 302.680908 115.336044 183.546997 154.149994 84.190521
## [2526] 96.039925 149.431412 158.047195 104.285828 337.191376
## [2531] 85.291237 233.874146 128.692627 108.980148 72.270424
## [2536] 166.756424 95.105652 177.371964 117.347847 139.509842
## [2541] 413.264740 140.433731 120.482010 91.545395 90.989479
## [2546] 259.884064 238.661758 111.247192 120.377441 159.201248
## [2551] 76.984161 74.313057 157.329178 179.781921 135.975128
## [2556] 119.990860 92.819298 187.482407 125.415466 96.746048
## [2561] 172.930466 125.194908 151.766388 91.830971 162.839706
## [2566] 110.504974 125.016609 153.402039 255.122604 176.853775
## [2571] 229.355133 170.875168 148.502838 124.252754 344.305908
## [2576] 159.069458 138.644699 152.031326 128.860260 287.562866
## [2581] 55.318420 217.399658 134.036362 97.348488 183.007187
## [2586] 129.617020 202.687103 71.296341 257.516266 125.796844
## [2591] 67.938095 188.607605 124.567688 76.775925 94.127327
## [2596] 83.632072 105.430138 97.919983 105.473351 197.610611
## [2601] 94.722542 184.091400 90.686584 137.495422 99.214516
## [2606] 111.205315 134.481079 149.391373 158.290466 130.015900
## [2611] 163.124084 108.474632 117.554855 81.232651 200.222488
## [2616] 103.771271 83.089119 102.387306 92.448250 163.478989
## [2621] 106.571419 221.613846 122.360054 92.388878 137.638596
## [2626] 99.702011 91.794464 147.166489 137.973480 160.894302
## [2631] 40.145988 92.626137 79.267044 124.673683 128.959106
## [2636] 200.004166 204.684601 188.807266 137.904282 138.257050
## [2641] 104.461220 78.673225 122.218292 92.036926 134.084991
## [2646] 121.937149 134.127762 140.225266 80.295761 71.197845
## [2651] 66.910103 87.082138 159.067749 128.921661 134.808945
## [2656] 179.061859 113.806908 105.293640 233.467239 100.846329
## [2661] 100.617432 198.450912 121.653603 131.536438 132.916428
## [2666] 91.903137 149.630402 161.717590 191.733841 115.869545
## [2671] 93.756767 99.120720 196.670914 68.969803 208.650757
## [2676] 297.003845 302.543884 130.757156 162.259827 27.933643
## [2681] 78.388802 130.925491 152.364777 93.196159 5.311580
## [2686] 235.601227 -7.752636 64.592422 121.919922 111.495743
## [2691] 47.060402 96.500839 361.030060 131.263245 90.697838
## [2696] 53.550236 82.467323 168.632294 219.293289 83.978561
## [2701] 73.595406 32.165936 296.468445 241.761185 85.490257
## [2706] 101.496048 227.516647 40.951103 138.407486 123.836105
## [2711] 134.214142 141.460220 218.973404 42.266075 112.785645
## [2716] 108.382835 178.799026 66.362282 148.917236 147.547165
## [2721] 118.811356 177.404007 116.058228 107.910721 352.303619
## [2726] 99.261711 130.458694 196.514786 240.275253 110.022758
## [2731] 124.722229 210.380707 124.886559 97.599579 84.709656
## [2736] 114.054413 186.335876 335.288513 131.833099 160.178314
## [2741] 192.065521 136.246399 98.410378 136.544907 105.307228
## [2746] 121.301071 139.674179 77.208076 149.245438 57.585564
## [2751] 114.483971 156.689255 145.588470 180.420013 56.821400
## [2756] 209.116013 283.041107 139.813004 83.891174 77.974434
## [2761] 68.499031 72.193802 133.066849 144.328049 120.249062
## [2766] 81.518845 113.058624 133.849915 203.442078 134.950363
## [2771] 77.563400 245.403854 138.368515 209.008667 131.739410
## [2776] 79.318695 236.721909 270.106201 117.987709 129.354355
## [2781] 115.417030 104.422379 119.595490 103.820152 139.932724
## [2786] 115.677826 140.348389 85.547600 205.684448 142.904663
## [2791] 143.941879 102.623840 114.806534 135.255814 110.302689
## [2796] 107.219315 164.483765 111.914528 96.275436 117.494385
## [2801] 111.774704 156.760818 138.150330 101.593712 160.538712
## [2806] 113.806282 135.739868 139.304214 166.546341 129.762054
## [2811] 165.041763 116.120102 135.372192 155.055008 266.609314
## [2816] 100.064934 143.594345 243.773148 162.271500 239.998077
## [2821] 152.969086 234.624146 136.181534 148.331070 79.240471
## [2826] 147.035507 225.786453 158.853149 70.107864 118.135483
## [2831] 206.275665 238.995255 125.033493 77.114975 71.856728
## [2836] 104.452728 113.426712 93.171188 134.787109 168.110336
## [2841] 69.405991 175.879013 118.644798 137.956757 158.274170
## [2846] 129.221222 120.387695 83.571854 80.721474 127.856941
## [2851] 96.868500 187.257187 155.310486 110.610306 110.685486
## [2856] 219.905914 157.472870 139.634598 114.828690 108.882935
## [2861] 123.753342 64.840706 127.587708 117.003227 143.781250
## [2866] 131.295441 121.454773 270.200745 164.165100 119.792778
## [2871] 51.198959 101.618996 122.375031 241.264069 109.459869
## [2876] 124.266914 178.881393 263.464020 82.346169 85.442856
## [2881] 152.506409 141.573318 175.991608 244.984955 98.935387
## [2886] 151.734238 95.961510 77.868553 139.842468 109.936707
## [2891] 92.076347 124.872589 119.466873 126.776024 143.621765
## [2896] 134.534958 262.389374 135.147202 148.882584 147.945831
## [2901] 100.855408 125.177429 114.710609 161.597382 118.218124
## [2906] 180.947266 98.278732 203.880508 139.561844 56.029934
## [2911] 115.654388 101.220016 122.975143 210.200897 86.384972
## [2916] 230.591751 56.410759 136.361130 102.056709 85.970802
## [2921] 148.347290 159.526474 27.806816 147.740021 138.929901
## [2926] 126.566818 161.580002 247.306931 55.763058 48.038017
## [2931] 131.144363 142.430984 78.876106 117.604385 179.635590
## [2936] 116.597397 116.856613 87.834389 105.605202 123.896088
## [2941] 57.842144 142.189835 147.974106 188.078812 224.771393
## [2946] 103.564224 94.453094 168.594727 198.016663 118.376007
## [2951] 123.881004 304.319458 48.136734 98.068108 91.988632
## [2956] 104.026382 127.617226 112.628967 127.770065 76.640167
## [2961] 111.877197 237.859299 53.923069 -4.439453 131.464127
## [2966] 108.396866 70.588913 59.192944 73.203896 321.023743
## [2971] 118.065834 169.999817 173.028870 182.608917 195.799820
## [2976] 135.908554 171.049408 133.686737 114.988075 152.622208
## [2981] 136.362854 194.230774 124.216675 111.564987 77.627930
## [2986] 159.380341 221.607590 156.712738 85.218987 365.060577
## [2991] 77.981071 86.985451 165.993469 86.783943 115.597244
## [2996] 94.601028 63.923573 126.818031 207.024170 175.920670
## [3001] 92.450882 97.365860 132.100784 94.704933 134.077972
## [3006] 179.837326 45.549168 107.054840 144.870316 72.865265
## [3011] 220.293701 126.887665 81.179688 67.474495 86.434952
## [3016] 229.836151 116.055702 149.099197 144.421036 119.145706
## [3021] 115.728493 124.271393 82.657509 102.751129 98.303391
## [3026] 172.850769 84.712273 157.286087 145.558487 129.149445
## [3031] 125.551834 106.700287 137.999329 111.386887 146.558426
## [3036] 138.878571 111.115120 86.577988 142.189514 61.392281
## [3041] 75.690201 173.231384 126.714035 139.826248 118.324577
## [3046] 138.171692 158.780716 97.651878 92.226318 211.072525
## [3051] 151.846878 156.925125 151.466568 120.403038 92.736526
## [3056] 260.426727 145.716583 111.431725 123.725166 92.187668
## [3061] 95.404953 187.020706 180.800629 273.843933 100.025711
## [3066] 205.486862 134.082275 156.419220 193.575256 141.692551
## [3071] 141.435608 135.011795 111.517288 92.831009 182.419586
## [3076] 71.197052 186.739166 239.757339 107.361000 101.230980
## [3081] 100.497665 83.983627 102.570290 128.674927 200.500183
## [3086] 203.959534 201.331772 142.646591 104.992073 110.339996
## [3091] 372.969574 131.628708 167.221603 161.771011 118.319695
## [3096] 117.795944 153.476593 76.548492 240.169937 174.228638
## [3101] 131.612518 152.294647 114.619743 90.641014 159.775360
## [3106] 109.547867 138.113602 66.913925 116.302742 92.852455
## [3111] 138.396530 102.239288 131.544617 90.350853 139.206070
## [3116] 96.662682 82.691002 137.050003 103.822823 128.509277
## [3121] 96.632729 292.299164 292.257446 157.626099 92.492920
## [3126] 199.461655 107.241158 95.645073 159.848206 94.159775
## [3131] 233.860153 141.638779 104.460670 221.425156 65.947151
## [3136] 82.717804 99.021492 129.161285 180.607986 141.757202
## [3141] 140.399597 73.281868 133.380966 167.942154 110.788567
## [3146] 110.968307 114.576370 138.405029 172.776352 165.604614
## [3151] 87.816032 140.488983 99.581528 88.436752 138.540253
## [3156] 147.147995 173.682312 135.329239 164.063660 163.372192
## [3161] 255.581070 84.354027 146.055801 140.572922 102.589699
## [3166] 93.325233 205.134903 67.087563 136.519379 106.525238
## [3171] 179.845490 259.357483 123.086014 168.030853 240.934845
## [3176] 79.767899 101.424301 165.199173 84.455215 264.867249
## [3181] 177.572281 184.929901 103.118332 183.141235 187.591324
## [3186] 134.617615 136.819382 117.836189 98.529579 159.224442
## [3191] 91.204277 164.410660 186.460022 222.461853 185.673569
## [3196] 113.401230 216.675079 230.012756 131.768066 110.048172
## [3201] 85.129349 141.622955 139.543762 166.745712 114.222549
## [3206] 135.642273 135.761597 143.114334 139.307877 119.216064
## [3211] 223.808182 192.251144 126.516914 89.772575 109.495415
## [3216] 99.964241 215.730804 122.591888 123.624748 173.325989
## [3221] 104.700684 59.113369 198.510071 128.073776 86.614510
## [3226] 86.209488 64.645058 124.591202 88.609390 160.647095
## [3231] 159.274841 94.120659 190.617050 125.239624 81.176872
## [3236] 185.738693 80.529549 184.398926 100.983139 156.041412
## [3241] 119.328514 103.759415 119.444359 129.019058 115.698654
## [3246] 182.748810 136.218063 168.169662 149.452744 36.097263
## [3251] 232.205353 116.814743 96.522644 114.646805 112.236992
## [3256] 108.291405 93.889618 102.369804 267.941284 109.215904
## [3261] 183.704819 148.756409 96.183617 101.033867 115.242554
## [3266] 145.368927 145.370438 248.341034 188.284241 142.410446
## [3271] 79.124832 144.581573 180.112106 144.972107 143.588242
## [3276] 102.131744 186.087921 138.881241 93.880608 145.558258
## [3281] 61.499699 246.354858 178.109100 156.934158 167.837341
## [3286] 102.928223 121.934753 101.288055 86.943047 112.327995
## [3291] 126.980591 152.208267 102.514900 113.002960 277.935211
## [3296] 118.250748 199.312164 237.164688 184.172195 60.513035
## [3301] 126.621216 187.929749 182.310028 91.448227 240.130783
## [3306] 127.096436 80.277397 145.400848 61.085487 176.831924
## [3311] 137.506241 187.991943 133.276672 113.401611 118.308487
## [3316] 127.470673 89.678474 164.216660 210.214355 123.778412
## [3321] 175.494797 151.444946 115.472107 95.568756 101.727676
## [3326] 127.396355 117.554459 69.194633 165.157364 115.846260
## [3331] 121.215630 122.521706 157.260925 134.890625 95.569763
## [3336] 95.242264 111.399681 171.757278 133.359253 199.902908
## [3341] 152.947113 82.705978 194.171173 173.401108 221.390381
## [3346] 130.788208 132.007736 157.364365 115.248421 127.868698
## [3351] 137.676987 114.873131 145.239273 96.552567 71.868538
## [3356] 83.447968 85.674492 121.601486 131.276306 209.722916
## [3361] 148.677261 131.280075 120.626152 292.808624 117.254745
## [3366] 104.629059 165.626999 97.529243 124.486923 111.990891
## [3371] 126.568039 242.028687 158.473145 184.374115 145.734619
## [3376] 125.810852 115.848419 99.948753 119.929520 67.085480
## [3381] 240.155792 73.694839 196.673721 108.864632 103.532745
## [3386] 172.234848 102.533661 104.046631 112.281754 96.496735
## [3391] 136.032486 276.310547 107.511490 133.432220 81.982857
## [3396] 171.349045 134.520630 142.644226 122.331924 111.256134
## [3401] 254.102905 81.647667 124.362747 118.777374 161.443588
## [3406] 93.401497 120.323212 138.087738 121.576973 80.405411
## [3411] 186.547836 203.922043 68.668236 147.336258 100.504410
## [3416] 102.955452 120.035919 185.092117 137.834122 224.151520
## [3421] 280.157440 69.344475 200.796356 153.524139 100.972946
## [3426] 107.724037 100.209442 176.714462 91.799728 83.334808
## [3431] 144.457474 198.315399 149.281143 138.802109 227.980835
## [3436] 148.133347 118.570648 175.985687 131.784988 83.860832
## [3441] 171.042877 121.517136 137.273956 213.687515 219.672928
## [3446] 207.334473 235.167511 142.150970 63.449539 78.329590
## [3451] 106.095268 168.218292 98.254364 122.552238 76.194725
## [3456] 184.228104 150.841431 119.750870 89.545792 184.856293
## [3461] 203.313782 174.049759 129.750732 134.001450 118.919540
## [3466] 302.212402 122.489685 121.286484 97.632195 170.736282
## [3471] 102.723808 91.470161 124.009453 85.153778 234.576706
## [3476] 171.053238 144.838989 118.144409 106.041862 133.068176
## [3481] 170.686630 155.128601 107.301437 127.766769 220.625534
## [3486] 109.407738 130.244186 136.282990 105.014244 143.080399
## [3491] 59.109707 125.806389 101.376541 105.227844 124.613518
## [3496] 142.404053 87.464638 179.053528 231.537170 86.091782
## [3501] 120.472321 305.244629 144.282990 97.627213 90.683380
## [3506] 81.520905 182.597382 98.938873 292.401123 96.831093
## [3511] 160.155487 90.656364 238.581665 122.698929 84.021797
## [3516] 149.219345 34.414391 328.837067 11.656833 -66.501045
## [3521] 74.386528 191.119598 -45.689293 141.023178 86.406723
## [3526] 177.574356 88.568489 95.580009 132.262817 25.000751
## [3531] 141.290863 103.282074 78.492935 159.918411 129.184387
## [3536] 49.643730 154.467285 96.879295 114.042747 78.476295
## [3541] 86.316444 82.505783 93.189377 184.805801 188.488586
## [3546] 61.995827 88.927284 118.118286 84.503967 75.141670
## [3551] 113.612328 118.529678 247.848938 122.883545 85.887146
## [3556] 111.610786 73.954430 93.926224 84.533249 207.591187
## [3561] 104.392204 107.919937 134.207657 299.488556 286.782837
## [3566] 328.090790 204.362991 302.290924 211.084229 105.126709
## [3571] 144.007034 309.095062 88.986137 439.499817 283.084839
## [3576] 133.791794 137.486877 110.883392 305.440216 238.360977
## [3581] 303.386871 98.181068 93.670242 114.110535 179.696457
## [3586] 150.312805 137.462082 103.174423 185.263504 110.535614
## [3591] 220.242386 295.322449 135.259872 299.580994 136.711319
## [3596] 301.243042 122.400574 298.141266 161.106461 283.050629
## [3601] 280.517883 99.463127 145.712173 293.542694 298.754944
## [3606] 244.335663 294.148651 85.818558 196.222687 306.430298
## [3611] 226.435974 441.657196 122.678062 148.626114 275.512085
## [3616] 181.420364 428.816681 176.600433 146.006653 312.404175
## [3621] 318.593384 410.917572 311.278412 126.165482 436.629120
## [3626] 116.992073 126.807327 135.081100 120.052620 296.549164
## [3631] 263.020355 299.498444 171.938293 238.942062 297.198975
## [3636] 245.609009 258.474091 92.033760 141.638535 302.339874
## [3641] 112.070915 123.836891 441.573151 229.390244 107.137817
## [3646] 133.229752 289.148346 113.629898 89.735298 293.627289
## [3651] 303.540314 134.500641 254.256012 115.996368 157.950607
## [3656] 297.417297 152.219772 294.334503 260.795166 79.214836
## [3661] 294.035065 133.667542 134.848618 160.146194 112.925385
## [3666] 214.228043 170.269928 293.705902 235.549835 293.198395
## [3671] 145.426804 182.679077 156.809799 98.055977 108.471062
## [3676] 325.897186 200.494720 127.192955 309.242310 167.715088
## [3681] 230.794556 111.736458 140.465286 167.427048 261.576691
## [3686] 203.868896 133.257294 245.324905 237.717667 161.953094
## [3691] 250.379608 229.428741 89.359589 86.149940 147.985733
## [3696] 112.049599 58.424442 130.540375 151.144623 177.074738
## [3701] 118.414230 88.716942 174.669128 140.089798 176.060974
## [3706] 131.708954 126.630722 120.449837 122.022644 97.433693
## [3711] 86.364433 295.703522 165.950272 111.577034 130.750473
## [3716] 165.292511 95.731155 124.215019 67.755112 105.283745
## [3721] 97.718475 111.949394 171.921005 196.894150 114.093689
## [3726] 120.037804 136.483536 121.767159 175.043976 174.104767
## [3731] 141.673279 100.599236 152.793808 76.932129 491.258270
## [3736] 189.736984 93.825455 160.737610 119.227798 98.522194
## [3741] 190.264069 139.702774 110.564644 333.285889 83.998817
## [3746] 148.486404 79.915306 356.844604 171.560455 113.762871
## [3751] 95.247337 157.757797 89.490181 168.056000 143.559250
## [3756] 78.236710 58.629715 89.758308 86.167900 104.688545
## [3761] 71.851562 75.178757 415.105255 63.740269 209.220795
## [3766] 206.489426 58.756050 194.551834 101.666565 90.737625
## [3771] 112.044556 84.389267 151.648270 210.935791 121.249657
## [3776] 59.847755 146.626785 90.676918 109.103622 58.929504
## [3781] 124.520912 115.396706 152.292557 147.382217 118.511353
## [3786] 392.805756 143.528732 170.105682 130.639862 126.911835
## [3791] 130.586655 148.671585 333.932007 297.584106 84.043892
## [3796] 152.617264 147.112701 181.241882 108.550598 174.248138
## [3801] 118.671600 73.071579 215.210861 58.852428 104.486282
## [3806] 110.936890 170.899933 309.408234 161.370667 81.132301
## [3811] 178.207687 130.431198 248.350311 125.730141 160.351807
## [3816] 274.404205 326.344208 174.192520 56.932922 255.520691
## [3821] 213.209671 82.404266 110.739677 105.537506 157.763336
## [3826] 123.834023 114.850548 287.670807 110.563972 96.227921
## [3831] 106.616776 153.801346 163.742874 130.107574 80.449677
## [3836] 69.847961 80.160110 40.904613 81.788963 113.724503
## [3841] 117.397049 89.247437 76.043312 127.248985 82.451164
## [3846] 99.272415 60.868649 70.975395 104.867210 200.264603
## [3851] 227.813843 99.202728 112.824089 103.790482 123.172943
## [3856] 81.707947 110.889900 131.540466 113.025101 140.485687
## [3861] 60.163975 93.401176 109.964745 106.689201 212.162903
## [3866] 84.826614 150.485397 53.789402 110.674103 229.494965
## [3871] 87.015160 281.331055 79.035202 152.432388 86.584229
## [3876] 276.160339 85.710709 187.741272 155.455612 115.249924
## [3881] 95.566978 153.912537 227.751419 107.087891 80.856689
## [3886] 99.305969 208.488449 662.804321 733.791260 73.803162
## [3891] 122.583961 101.004448 72.120140 25.649782 73.990509
## [3896] 96.023476 147.204697 77.099060 84.199348 110.551659
## [3901] 102.711494 185.386047 181.868347 117.476646 110.062119
## [3906] 113.219193 670.834229 158.237610 176.417892 116.883736
## [3911] 134.143112 166.133087 148.776215 137.934082 73.946503
## [3916] 136.380859 154.064224 125.258911 120.946724 117.458870
## [3921] 103.518814 100.664574 116.272774 116.511200 213.445587
## [3926] 137.687546 151.701355 175.827118 216.379120 125.700050
## [3931] 190.096252 77.265503 107.889481 107.115005 89.380524
## [3936] 172.264709 180.222977 127.174637 116.264511 155.938522
## [3941] 66.979774 142.367447 109.493172 128.358047 118.425858
## [3946] 444.198364 170.424683 118.161613 133.069885 155.596451
## [3951] 97.932243 278.813995 132.433197 137.785599 286.847198
## [3956] 84.883255 144.694199 90.624512 117.029984 197.695435
## [3961] 241.039627 135.833633 135.500214 130.104919 224.597290
## [3966] 110.062576 125.047493 132.679535 93.262146 192.299759
## [3971] 152.592285 83.039299 161.699539 186.798203 176.270401
## [3976] 123.652176 165.636505 96.319366 135.948547 93.762054
## [3981] 124.902336 208.432205 181.388748 107.228432 199.103134
## [3986] 112.354706 128.653503 163.667984 113.140808 143.626846
## [3991] 100.330383 140.284958 209.442474 205.452789 196.410538
## [3996] 186.030624 94.223778 200.595383 178.970779 303.162903
## [4001] 135.461060 183.384872 65.745209 123.062042 125.775856
## [4006] 281.588257 112.672531 154.346542 115.901825 212.862335
## [4011] 267.171722 75.051422 148.451385 215.054062 111.622108
## [4016] 265.174866 94.653542 104.000473 107.808289 130.493301
## [4021] 109.852722 140.095993 126.968643 101.789001 92.340050
## [4026] 254.564743 197.196671 103.990219 175.205627 171.925888
## [4031] 149.029434 152.498215 227.061752 170.450165 317.023621
## [4036] 126.845482 121.765305 354.076324 147.005463 190.979614
## [4041] 172.881119 179.159454 87.719666 174.835480 155.484619
## [4046] 77.094566 160.270584 113.599716 89.766800 133.498230
## [4051] 136.943176 253.088638 106.305519 154.789810 168.607452
## [4056] 188.522079 172.183121 194.743713 196.020004 78.381500
## [4061] 95.924507 220.114670 142.961746 162.358063 327.359924
## [4066] 210.981628 81.138466 63.486835 69.403297 96.501839
## [4071] 303.014954 237.126678 112.207336 235.635254 232.384537
## [4076] 141.244263 116.997925 331.239899 39.596008 127.787155
## [4081] 84.938965 133.640930 100.096130 264.346893 166.702103
## [4086] 73.506088 111.258850 207.875381 172.094269 152.840988
## [4091] 159.742874 138.184586 131.310150 99.805122 185.972656
## [4096] 158.906174 105.534767 235.479156 94.216438 65.671722
## [4101] 89.845818 190.424454 209.041306 178.399246 464.692352
## [4106] 207.878189 112.901665 199.122620 66.801575 113.660049
## [4111] 166.960373 83.029381 216.689255 153.237595 152.791443
## [4116] 104.279099 143.994049 85.655769 33.570251 196.988800
## [4121] 258.121979 79.893234 502.744690 48.396030 193.206940
## [4126] 184.627960 136.250916 175.506546 284.548676 342.594269
## [4131] 119.151100 102.037109 159.414047 195.502106 95.058754
## [4136] 105.800331 213.323868 87.764069 151.065781 163.688843
## [4141] 146.472549 109.626343 80.989693 194.614624 84.408813
## [4146] 67.477058 97.576355 380.273285 168.936798 149.138077
## [4151] 168.088928 99.125374 132.302277 133.716110 88.894279
## [4156] 198.263794 85.653893 153.306076 167.975204 81.706200
## [4161] 124.255676 158.168945 152.975143 72.287529 238.215714
## [4166] 114.553123 108.751389 119.881729 162.800720 256.019440
## [4171] 192.214600 125.192963 154.312515 127.475945 144.437668
## [4176] 197.353760 119.108093 86.447266 181.444336 115.626541
## [4181] 120.319855 126.847305 119.303711 199.258026 155.291687
## [4186] 100.073380 142.119385 133.342743 132.915314 133.059158
## [4191] 91.042435 163.530502 -27.697807 75.251610 144.754700
## [4196] 16.548416 118.572403 141.806671 261.765228 158.289322
## [4201] 165.546448 195.857819 157.616089 108.542244 163.307724
## [4206] 277.529144 117.822113 88.865913 68.681969 123.831741
## [4211] 104.124054 147.184952 185.383392 101.732834 178.178940
## [4216] 163.879120 98.072182 217.713028 83.702713 56.580265
## [4221] 82.166298 128.905212 85.107681 181.255081 311.970306
## [4226] 183.461624 97.787819 75.705307 131.362152 116.982483
## [4231] 126.970497 144.268692 114.106331 68.794884 157.325089
## [4236] 117.431992 119.698883 206.386276 176.716782 139.562988
## [4241] 190.025558 112.404388 185.247894 136.459274 174.326431
## [4246] 158.751907 132.415482 149.937607 92.217964 135.707214
## [4251] 156.908310 220.349960 179.010834 217.418839 113.876678
## [4256] 114.355537 157.021454 87.125824 124.822426 87.419106
## [4261] 197.469757 162.608292 100.126938 131.229767 152.489014
## [4266] 247.679932 108.106514 134.050400 170.563599 83.661621
## [4271] 141.250244 190.777328 111.775215 147.628677 100.427361
## [4276] 156.986649 119.210304 252.880692 133.379913 195.573349
## [4281] 171.172226 113.670776 135.981537 139.356369 120.094566
## [4286] 155.460281 231.218506 123.158646 89.597771 88.690010
## [4291] 129.785141 196.192291 114.474701 207.539444 101.061798
## [4296] 128.101593 157.792465 158.394196 122.546875 146.046860
## [4301] 129.235596 238.022583 218.354355 73.385880 94.878006
## [4306] 63.563442 151.574707 200.760437 85.027275 164.799332
## [4311] 64.842117 107.707260 90.875282 224.700180 69.088844
## [4316] 57.567574 124.838692 107.018723 148.546631 114.173256
## [4321] 145.110107 164.630356 96.461319 95.319756 177.668976
## [4326] 94.338570 163.920593 89.087784 120.286453 167.257462
## [4331] 101.854912 125.179520 180.314758 89.009613 85.042931
## [4336] 202.798752 125.267609 127.066544 95.860382 111.586281
## [4341] 255.905396 167.949188 144.267944 109.262497 136.008118
## [4346] 135.708054 216.759872 91.472816 105.757088 125.364861
## [4351] 71.360100 50.185635 70.938408 147.157608 94.495872
## [4356] 106.646255 100.770744 94.700119 223.029007 123.281662
## [4361] 119.855415 177.493164 50.360573 131.579926 121.812370
## [4366] 161.100601 186.150818 329.608582 147.479324 68.794662
## [4371] 127.101227 157.513611 152.652481 78.814186 181.123947
## [4376] 108.603477 143.441269 187.600388 135.187332 136.969482
## [4381] 122.444901 89.481316 196.953217 122.233810 120.125961
## [4386] 133.195450 113.352707 80.876030 148.718933 144.992615
## [4391] 59.969147 76.631866 140.588531 125.004799 94.884186
## [4396] 211.873306 120.175697 172.714523 164.943588 139.805054
## [4401] 275.575928 146.081329 132.768539 141.724289 159.755478
## [4406] 115.358994 132.949829 152.448105 258.418549 180.647934
## [4411] 340.502991 125.673317 138.147949 119.496223 100.608574
## [4416] 118.765869 125.150833 271.881653 189.537109 203.719543
## [4421] 129.735077 181.067215 74.102959 93.822037 112.657860
## [4426] 72.376617 82.963013 109.366966 133.266632 98.471733
## [4431] 137.681473 221.699860 108.832542 157.704361 85.656837
## [4436] 202.482803 142.926224 158.147415 95.769951 145.519241
## [4441] 171.069305 144.533539 119.712173 139.530716 162.267319
## [4446] 99.523506 135.059021 93.034332 117.814865 136.420212
## [4451] 526.765198 151.575760 139.561234 171.974792 145.062775
## [4456] 86.106331 196.199585 175.748398 204.885345 120.592628
## [4461] 115.338974 112.352470 234.722610 116.674179 161.487518
## [4466] 347.325745 94.873955 83.964546 311.643250 71.369530
## [4471] 205.727036 99.350327 187.319885 173.410995 88.664185
## [4476] 192.383331 95.901665 176.844696 151.123459 -75.647354
## [4481] 74.585007 175.488968 120.610672 80.856148 122.859917
## [4486] 74.670166 146.852997 163.685074 161.203049 171.451767
## [4491] 334.572327 112.670074 212.964828 73.942223 109.636681
## [4496] 120.847954 85.596420 172.921158 257.954895 108.254745
## [4501] 120.509109 78.290901 118.983963 176.992737 87.415276
## [4506] 79.325882 260.871368 130.465302 114.839813 107.796051
## [4511] 131.578796 97.480499 92.339783 181.696381 155.999619
## [4516] 42.911236 174.489090 149.231369 127.626541 408.753845
## [4521] 88.658005 194.125778 139.456909 209.641235 105.299232
## [4526] 168.352478 118.810181 110.647141 74.169312 325.869354
## [4531] 86.327888 128.807922 148.838562 86.364594 207.846024
## [4536] 90.530190 135.579773 101.092712 50.634007 144.719025
## [4541] 158.073425 191.487671 123.704849 78.248543 242.581299
## [4546] 192.056381 211.996246 80.258156 116.513237 105.143616
## [4551] 94.543724 117.251152 91.296989 492.298370 98.382538
## [4556] 93.137573 85.378639 145.400238 147.320679 127.080200
## [4561] 199.666473 146.668671 157.517517 153.067886 99.136452
## [4566] 113.485756 77.779762 31.136150 43.080017 148.377975
## [4571] 208.378479 228.157822 116.939072 135.332336 84.627251
## [4576] 72.512802 77.083778 76.541214 129.435135 73.674881
## [4581] 129.994812 99.285400 286.620056 132.674057 181.402939
## [4586] 105.322662 97.745232 139.316940 121.379784 142.052750
## [4591] 233.666290 82.821213 82.596344 92.870010 195.930908
## [4596] 273.239044 145.103088 310.476105 125.635353 76.255409
## [4601] 213.488037 94.763588 111.094627 131.865173 86.805275
## [4606] 152.514359 102.511833 116.351654 81.829788 195.070694
## [4611] 189.363541 98.558098 193.118454 123.035904 93.730492
## [4616] 185.290436 157.236176 74.563065 146.620789 239.480957
## [4621] 96.545097 304.552216 69.748627 107.423462 140.133865
## [4626] 127.971771 151.903961 130.006668 149.323334 100.983742
## [4631] 102.521622 78.108467 154.366882 89.868523 122.362419
## [4636] 95.737129 54.647163 123.342560 147.871536 95.508583
## [4641] 103.794060 142.348602 74.756042 112.836723 187.514771
## [4646] 132.083786 112.999069 128.132401 206.063965 179.113556
## [4651] 237.837936 178.512665 70.431618 141.529373 83.833176
## [4656] 201.819351 90.280197 112.545914 148.718842 85.781013
## [4661] 106.070724 181.653290 97.898682 134.733215 58.611618
## [4666] 115.629448 159.206680 289.573517 108.829010 84.747375
## [4671] 502.572449 -47.310905 106.673874 189.608185 80.641418
## [4676] 299.838074 95.855423 156.542145 153.915878 106.875168
## [4681] 103.287971 182.985718 166.486755 85.009270 99.003296
## [4686] 116.904610 139.129318 94.912277 159.625229 103.842827
## [4691] 102.263153 172.071198 132.262299 72.096581 85.710732
## [4696] 41.369705 128.046722 124.481895 119.957565 189.995743
## [4701] 232.212357 126.610565 79.023201 117.634216 100.581497
## [4706] 137.785843 128.363678 319.216095 196.332245 330.115692
## [4711] 211.145676 89.631477 95.143257 97.049568 90.657127
## [4716] 231.256531 121.656647 164.113586 142.184769 100.006760
## [4721] 203.734039 112.754135 107.538803 71.521660 94.519157
## [4726] 109.030891 163.486893 77.454819 106.580460 105.180893
## [4731] 106.537239 147.850220 72.979309 74.151283 231.519669
## [4736] 169.342361 107.859848 99.573158 86.545753 124.869537
## [4741] 118.585258 192.248810 223.990234 232.876175 84.475182
## [4746] 104.136459 117.258575 105.238670 97.428505 156.152893
## [4751] 104.881767 96.945564 111.165421 122.270714 119.721443
## [4756] 115.448158 127.677536 124.383438 120.459625 90.334869
## [4761] 92.343269 120.967346 144.222458 67.553474 146.180115
## [4766] 88.845917 111.785316 84.948517 53.750729 83.286942
## [4771] 56.719391 79.843033 99.738930 114.496735 113.845108
## [4776] 105.815865 109.658340 148.231674 245.994064 79.241882
## [4781] 152.241623 191.358383 107.806229 178.451370 145.804520
## [4786] 174.666656 123.055946 306.212738 138.651077 99.843018
## [4791] 145.998489 153.358047 68.538628 82.911652 97.511520
## [4796] 88.962471 195.511658 160.888290 141.725006 107.831734
## [4801] 175.210037 98.031479 60.527588 91.140930 164.852585
## [4806] 175.648254 78.779121 37.473850 127.069405 123.760704
## [4811] 111.520721 110.474197 131.303696 363.254822 125.388512
## [4816] 156.173767 174.504089 79.470879 107.160896 84.366875
## [4821] 99.703438 209.117508 98.082901 119.267685 94.741791
## [4826] 114.052834 105.874779 64.247421 129.573608 77.404778
## [4831] 100.184624 101.953316 160.648590 93.005432 118.901550
## [4836] 145.379166 89.772194 99.049973 74.408234 68.365585
## [4841] 81.289146 101.352890 74.612000 133.759354 193.914673
## [4846] 83.236038 146.108109 137.457642 61.139866 128.502960
## [4851] 97.256317 235.184998 128.153442 137.161606 129.020233
## [4856] 401.608032 71.055092 107.067535 149.860428 115.322418
## [4861] 127.554626 250.430984 121.678055 126.876213 90.266548
## [4866] 104.575294 68.605118 146.002899 133.535065 86.526909
## [4871] 143.870010 136.748993 114.056610 116.470482 135.958237
## [4876] 97.926201 114.291389 134.677475 144.130920 124.170746
## [4881] 199.261856 135.669769 181.401474 273.379944 114.601868
## [4886] 120.410072 184.383820 158.806610 86.989899 100.437195
## [4891] -52.254517 95.651421 141.028595 68.614204 209.519806
## [4896] 190.355957 72.227547 125.424545 84.674957 109.678864
## [4901] 136.292557 123.184967 146.184830 189.228119 148.162674
## [4906] 36.233063 141.118286 110.527428 103.026505 127.550636
## [4911] 88.818108 172.522888 147.365509 241.299255 75.851395
## [4916] 105.529968 93.129196 85.180717 132.516296 88.383347
## [4921] 103.590416 87.924522 161.975189 98.718681 75.537231
## [4926] 109.502197 116.026718 102.269341 77.833755 118.184082
## [4931] 132.327652 96.958565 159.455002 122.951591 123.436066
## [4936] 103.226357 162.067139 114.040810 95.839325 112.833115
## [4941] 146.760239 92.349396 257.442627 112.064972 165.146225
## [4946] 81.595604 143.438126 89.752708 106.513077 95.155144
## [4951] 129.707626 100.326012 90.218613 143.252319 132.538025
## [4956] 157.614929 70.780846 157.300385 131.110840 321.478729
## [4961] 337.385010 140.497620 101.979889 92.626747 201.004852
## [4966] 103.002686 152.012192 42.809822 145.068390 108.839989
## [4971] 94.895645 103.308609 142.658081 117.878082 57.097446
## [4976] 61.850567 105.238197 247.985367 144.208298 101.442101
## [4981] 145.455612 111.824921 227.516556 293.648804 216.783646
## [4986] 142.498810 89.872124 213.259766 99.620941 81.816483
## [4991] 177.533127 190.328369 192.253098 169.672165 129.543274
## [4996] 128.490295 109.339600 103.115555 79.228127 104.827194
## [5001] 125.264786 90.887695 353.455231 142.838547 143.088242
## [5006] 144.523941 188.243973 95.947510 109.320457 172.720093
## [5011] 285.791199 114.709885 92.017593 82.235222 114.069916
## [5016] 129.822098 125.333099 61.458740 187.371414 165.201599
## [5021] 28.323483 109.030869 151.618958 232.180359 191.831467
## [5026] 266.052277 134.530807 371.327179 146.560455 78.464310
## [5031] 123.023476 106.688026 184.109009 96.747711 211.530228
## [5036] 154.411926 83.066635 209.294815 137.104111 154.975143
## [5041] 226.953049 135.280518 137.646103 56.370438 142.083405
## [5046] 223.255493 184.403381 187.502625 47.406513 98.158485
## [5051] 179.505493 194.022507 109.130119 87.359871 127.000397
## [5056] 274.866821 153.289825 149.528671 134.271301 140.189911
## [5061] 108.045914 21.532314 73.093277 139.538589 189.233566
## [5066] 93.626190 52.194378 287.179138 79.081215 92.153694
## [5071] 93.156380 232.295609 175.525726 107.963463 155.723694
## [5076] 109.946465 71.781006 94.973251 207.952194 36.066109
## [5081] 172.229630 199.561783 114.938599 142.400406 164.363495
## [5086] 111.682159 98.298401 170.228638 82.339996 95.713860
## [5091] 225.217575 93.044090 159.240295 103.734344 315.616302
## [5096] 252.534302 126.791000 131.082504 294.565155 91.134491
## [5101] 159.464294 101.519936 174.608398 315.124207 80.727158
## [5106] 122.535965 139.298615 149.295456 128.867752 82.444778
## [5111] 194.727722 112.628174 88.857315 79.630669 98.139282
## [5116] 82.255486 109.082733 198.075150 145.882202 147.766724
## [5121] 105.627640 202.300705 101.388321 67.803558 69.061508
## [5126] 114.313797 78.953094 94.500793 256.457703 156.488388
## [5131] 224.557877 219.776428 65.877945 100.001785 111.981613
## [5136] 95.009842 151.943314 364.790253 364.970093 183.807281
## [5141] 158.303864 109.214096 189.944244 90.007576 315.988281
## [5146] 144.492630 84.713890 108.976257 190.231598 112.076981
## [5151] 129.385544 85.150063 170.314728 96.826813 165.080307
## [5156] 82.072815 94.143829 134.281952 134.488693 163.536057
## [5161] 251.242966 293.400330 29.975395 122.655594 106.504166
## [5166] 252.923859 202.206223 149.006927 275.459442 90.503143
## [5171] 128.235764 107.144630 115.531944 80.659752 153.813293
## [5176] 177.983383 104.979797 74.276314 186.631561 106.404022
## [5181] 78.391800 92.475433 99.372940 278.208618 161.710739
## [5186] 227.883087 72.163681 195.143173 132.945068 186.010315
## [5191] 122.971161 130.631882 260.327148 48.887375 251.515686
## [5196] 178.229858 114.088196 175.059097 140.564484 144.749832
## [5201] 118.022385 124.549843 180.152969 146.840363 58.804409
## [5206] 180.047058 103.911530 134.470810 139.655624 126.096939
## [5211] 129.017532 194.547653 103.960243 104.466408 149.685059
## [5216] 29.972740 145.119888 111.748466 232.771194 117.164162
## [5221] 224.710617 27.202560 91.405396 148.535660 90.503387
## [5226] 283.357697 148.718079 124.968483 94.452042 214.837448
## [5231] 114.789093 94.913651 103.776199 74.076820 84.740036
## [5236] 55.916145 171.245270 278.687744 133.380325 141.099335
## [5241] 95.965919 157.343384 57.998756 203.360443 41.444519
## [5246] 133.938004 107.789909 107.502365 126.802673 118.299957
## [5251] 213.620499 139.400192 10.812244 172.952896 141.331177
## [5256] 211.604706 156.373550 115.606102 110.652679 205.594879
## [5261] 68.772324 61.483932 61.353516 78.561676 75.724625
## [5266] 167.850372 115.395996 155.092392 165.671692 106.677353
## [5271] 60.218525 153.028793 138.630478 108.292244 170.264023
## [5276] 95.066887 33.820984 83.041969 169.451828 205.161102
## [5281] 134.349930 176.938446 169.029022 200.325684 50.006828
## [5286] 71.000801 225.839706 90.579315 133.837784 49.297150
## [5291] 95.713676 277.432312 100.718773 254.455780 162.765213
## [5296] 181.606995 173.390778 91.524773 149.002502 129.102554
## [5301] 196.765991 101.057999 203.157578 134.612671 120.763573
## [5306] 165.862869 157.897858 104.498413 186.895828 138.743347
## [5311] 150.697998 167.774445 136.786591 119.867592 71.496277
## [5316] 120.557175 145.985977 309.295349 226.793808 76.794594
## [5321] 152.833694 184.297989 73.278709 152.206711 218.101776
## [5326] 117.628609 196.320312 101.803482 159.293213 148.046005
## [5331] 133.070953 185.319382 321.972321 142.239685 110.164841
## [5336] 129.760208 118.731125 144.891800 102.745628 34.463814
## [5341] 235.021164 124.525391 118.371880 93.012672 91.253716
## [5346] 97.750237 127.686768 98.279533 325.531464 299.618469
## [5351] 147.349625 225.305008 159.078842 138.570297 92.581871
## [5356] 88.326797 78.817802 142.999481 91.188683 139.612061
## [5361] 110.218033 171.148819 78.826843 195.144623 96.442322
## [5366] 86.312744 69.896942 128.465073 125.555496 127.247017
## [5371] 160.063263 86.924957 56.597599 154.040802 172.480316
## [5376] 211.248627 106.059799 140.365189 73.307594 131.344833
## [5381] 65.128487 247.570969 136.588745 1.643243 292.680786
## [5386] 129.292419 111.492859 91.839645 219.107468 99.668976
## [5391] 149.764496 51.964520 120.887039 143.111923 131.522736
## [5396] 115.600822 72.609116 111.949425 93.662277 218.087540
## [5401] 279.509796 107.650970 111.889359 69.287918 138.230194
## [5406] 158.889008 184.005508 236.598328 83.716393 356.678558
## [5411] 321.735748 150.223465 98.764587 85.054970 100.155693
## [5416] 87.884544 101.818634 115.807243 54.302517 82.581848
## [5421] 136.212616 106.922440 138.246490 103.962616 418.373474
## [5426] 84.598198 87.898895 188.380325 92.190903 215.205429
## [5431] 106.541306 115.750595 81.504036 99.521988 204.492752
## [5436] 113.132996 158.482422 126.564064 270.776031 163.769806
## [5441] 138.982498 162.988663 231.002792 151.109497 84.239662
## [5446] 192.395477 63.062809 78.746155 120.262421 100.138359
## [5451] 131.577225 263.785522 81.249397 127.165085 80.309441
## [5456] 132.260757 123.195358 107.461929 76.914528 66.157639
## [5461] 69.886818 201.456329 120.483063 139.817413 121.880699
## [5466] 145.925018 134.235596 131.396866 90.996330 135.245438
## [5471] 83.095268 127.846657 299.743103 128.400208 129.882751
## [5476] 165.804504 64.137299 76.134888 155.560349 107.933144
## [5481] 117.749626 103.721962 94.410713 141.556808 104.278328
## [5486] 125.416840 86.688255 83.895065 124.070541 86.513504
## [5491] 161.361374 105.743736 124.627823 100.516960 123.341782
## [5496] 181.917908 87.961212 120.165291 51.038620 229.857895
## [5501] 88.557846 28.524067 159.901184 83.223061 107.788269
## [5506] 23.096928 205.194229 197.590225 114.191452 100.703728
## [5511] 165.389664 84.554703 74.391083 133.925598 57.194370
## [5516] 72.999458 186.429779 107.339035 200.770065 158.912384
## [5521] 83.176826 74.873314 194.178223 194.925674 25.200619
## [5526] 101.808121 116.871704 150.882996 206.981369 190.310883
## [5531] 123.397102 123.848816 124.176048 234.668045 507.951019
## [5536] 298.816254 94.800430 187.363480 117.111855 348.630005
## [5541] 152.826263 114.143158 263.768768 94.557594 51.817688
## [5546] 260.590057 125.969162 106.592133 117.021362 123.126007
## [5551] 84.368965 89.682693 17.268791 168.259338 101.214775
## [5556] 180.220413 82.114326 134.033844 115.033730 87.864670
## [5561] 131.114792 152.399231 119.329231 105.490082 51.690331
## [5566] 157.113983 119.615456 138.608398 169.382645 92.562531
## [5571] 129.450134 153.276581 162.905563 125.963852 287.082520
## [5576] 313.941132 452.875397 137.768509 123.916916 104.094841
## [5581] 128.436005 293.116486 70.620613 119.178246 69.441788
## [5586] 170.978928 118.688324 309.287354 163.886444 78.789185
## [5591] 246.678101 171.626587 178.780548 70.886887 225.468765
## [5596] 90.678391 118.228867 138.983795 101.070007 102.119064
## [5601] 109.748596 189.613037 94.713402 136.629364 120.005646
## [5606] 106.314873 93.982185 120.885605 77.921532 98.714462
## [5611] 121.271446 119.480232 135.759155 110.812866 73.083199
## [5616] 61.489544 125.685677 170.905014 81.095490 172.268158
## [5621] 303.284058 86.786438 177.402679 127.853027 122.241707
## [5626] 94.595734 177.215347 105.214920 68.901413 133.811996
## [5631] 119.499191 176.967743 90.002655 127.842873 279.286621
## [5636] 161.397903 89.119583 81.033813 118.629761 104.464203
## [5641] 102.027382 151.852493 120.216476 128.361008 112.174492
## [5646] 119.702988 249.989639 262.001343 109.443497 130.805771
## [5651] 247.226044 117.114822 114.662827 120.419220 88.068771
## [5656] 141.264572 126.976547 140.833572 167.639786 117.441376
## [5661] 184.575745 152.811554 188.159912 174.624542 139.033310
## [5666] 236.109802 391.423096 167.112030 188.059311 212.988983
## [5671] 123.248184 150.304398 246.490936 128.025955 105.381104
## [5676] 86.080673 185.596542 170.123489 108.090515 153.677246
## [5681] 172.987137 119.751076 158.681335 75.857727 226.291611
## [5686] 128.142502 98.837173 271.060455 204.212738 90.727287
## [5691] 116.354614 181.269897 121.714882 197.392853 502.349182
## [5696] 112.443649 198.476181 131.548325 211.944778 107.889633
## [5701] 136.146255 222.425293 112.247192 118.156250 84.865265
## [5706] 214.785965 430.123322 86.836449 276.285736 139.526276
## [5711] 276.173523 158.455475 204.613159 142.173721 80.173340
## [5716] 205.490601 249.775986 236.250320 118.865875 206.789459
## [5721] 89.753510 271.190613 98.012032 133.493347 284.007172
## [5726] 201.435318 198.884323 229.354370 104.949997 118.019897
## [5731] 196.280685 166.750320 187.526703 285.159088 222.628632
## [5736] 126.290695 184.812439 63.496552 187.715790 127.424637
## [5741] 190.740707 223.937943 182.918839 130.301865 90.118973
## [5746] 91.764549 127.978821 373.453644 236.233932 557.499207
## [5751] 122.089523 183.045578 258.706757 109.611526 106.810226
## [5756] 122.989403 138.683167 127.231842 176.996338 147.848724
## [5761] 103.546555 128.269073 157.467499 317.495117 112.746727
## [5766] 154.220795 95.744087 82.722511 144.211533 172.316666
## [5771] 145.201065 101.558632 177.259567 174.060760 111.639816
## [5776] 103.506332 137.580063 98.701447 108.518005 161.547623
## [5781] 187.394104 147.687714 230.281769 368.227020 225.918045
## [5786] 118.190666 70.659645 50.885178 117.646729 127.076469
## [5791] 114.569099 148.042786 240.159729 75.268555 92.505020
## [5796] 51.394875 110.600853 93.386284 342.303833 132.069519
## [5801] 142.792816 120.586304 129.570328 259.354370 304.969635
## [5806] 87.463440 234.824280 89.620773 363.098328 61.012878
## [5811] 128.425659 94.736687 168.505981 153.920853 499.394196
## [5816] 175.180618 138.314468 109.378708 185.660767 88.136551
## [5821] 119.017517 110.171875 261.063446 145.908020 69.210533
## [5826] 193.160507 152.303925 136.356033 61.419815 90.001404
## [5831] 75.212967 125.389587 144.855270 122.780945 65.710358
## [5836] 218.864441 116.708023 55.646267 133.757385 267.803925
## [5841] 118.834930 88.779182 229.903992 46.375763 -26.976536
## [5846] 107.844505 80.187294 157.969711 61.181686 103.937630
## [5851] 89.118507 52.950935 135.866119 132.992233 138.925308
## [5856] 156.516022 391.574768 153.904465 170.604813 104.677834
## [5861] 74.613831 203.613510 180.774063 134.849014 116.264175
## [5866] 95.750069 125.848122 115.052200 105.685623 104.354828
## [5871] 99.717812 42.345436 91.757988 104.270821 129.285385
## [5876] 152.118134 92.573227 226.426117 124.736435 130.810547
## [5881] 113.566711 107.763908 153.127304 104.333092 110.643753
## [5886] 176.497009 131.926407 71.533066 130.960220 178.983109
## [5891] 176.322968 104.634651 185.953583 69.143837 184.460220
## [5896] 102.271111 218.834702 99.309341 170.433167 113.727287
## [5901] 244.624237 115.664566 141.539871 117.998802 149.336655
## [5906] 111.512260 69.479561 140.329376 169.604416 156.983612
## [5911] 80.999245 228.290497 100.907127 206.876419 233.420761
## [5916] 120.853294 121.632248 81.652840 107.560989 135.439041
## [5921] 137.774017 117.716148 180.862122 176.045914 191.047241
## [5926] 133.072723 115.835297 91.866196 93.664177 218.004425
## [5931] 82.653061 123.394226 152.491394 212.807144 80.511002
## [5936] 123.514313 102.092079 118.190605 177.925308 312.604492
## [5941] 59.773605 175.898926 139.444077 153.402344 123.231560
## [5946] 164.077881 79.822746 69.498581 185.181793 148.594543
## [5951] 148.150284 217.020126 52.626907 126.418472 166.601974
## [5956] 89.134605 124.491798 119.270210 132.817749 213.352936
## [5961] 142.226059 106.905327 96.816696 106.307037 118.892776
## [5966] 200.670609 109.316055 86.390076 116.745758 92.183578
## [5971] 188.719742 105.154991 133.789322 125.504158 94.043503
## [5976] 109.836472 122.864128 137.674973 219.795227 162.318146
## [5981] 131.968964 173.711563 163.765320 153.186096 141.596420
## [5986] 66.498299 123.100677 70.934746 126.690796 144.159851
## [5991] 105.238602 188.793182 105.071373 116.158852 97.992371
## [5996] 117.217323 72.044685 80.191719 131.220520 100.970001
## [6001] 111.450165 179.322418 168.766739 42.837837 110.294731
## [6006] -46.092648 195.800537 95.011322 64.971191 210.454697
## [6011] 113.989868 149.470825 84.890381 216.595627 104.875244
## [6016] 85.633034 103.067696 234.260712 159.422195 125.819336
## [6021] 140.732346 -57.279068 65.752457 117.046417 83.570457
## [6026] 101.152527 120.467117 100.661232 -52.048534 65.868187
## [6031] 137.667923 107.543762 92.605934 86.810196 211.153793
## [6036] 103.640343 81.152451 -50.966610 175.391464 134.144775
## [6041] 211.070236 151.440048 207.589813 147.571518 39.006832
## [6046] 130.236603 188.101257 93.630836 105.040131 171.534393
## [6051] 77.884346 107.986610 73.938484 86.020882 91.836784
## [6056] 127.524139 107.047440 288.564911 80.260666 150.286148
## [6061] 120.747070 105.990883 119.412987 161.286407 109.961060
## [6066] 63.758717 133.983963 70.889801 99.312256 177.555756
## [6071] 167.882675 100.238930 157.321976 129.746384 137.984192
## [6076] 82.877380 87.030350 180.061539 108.106934 115.011330
## [6081] 100.362732 131.343887 144.908936 71.278503 134.785217
## [6086] 139.453323 61.217762 102.781013 121.494919 216.315063
## [6091] 86.870712 74.821999 169.874359 153.402023 86.114700
## [6096] 117.059029 220.776428 112.818794 73.893837 56.254822
## [6101] 177.639633 135.254623 290.983795 221.170700 119.856277
## [6106] 253.602570 76.689224 109.488167 115.074463 154.737778
## [6111] 147.895020 158.314606 101.392784 220.933655 167.868744
## [6116] 89.618790 245.189011 70.192207 93.197540 159.488586
## [6121] 94.401604 140.415268 189.298019 139.208221 469.591248
## [6126] 190.947449 258.364471 161.280289 269.181335 117.211914
## [6131] 204.335861 190.640228 158.223541 103.366791 154.313919
## [6136] 166.732697 132.216217 112.908157 96.882103 121.416466
## [6141] 212.281647 276.347168 246.526535 245.682358 238.243607
## [6146] 142.544128 54.362480 253.127701 142.343063 150.908142
## [6151] 170.951233 90.365669 233.938995 142.181931 117.011208
## [6156] 138.595367 165.695160 114.064201 202.137161 105.135345
## [6161] 295.378937 194.763153 189.025314 217.537933 166.820084
## [6166] 156.956345 211.890717 169.711517 177.248108 212.252304
## [6171] 142.292755 144.985687 164.755737 152.519928 90.636833
## [6176] 140.842636 254.154297 105.825851 88.225449 196.916901
## [6181] 163.910248 131.501160 51.395672 315.123871 366.938202
## [6186] 238.036179 118.663780 175.383194 134.095200 663.469482
## [6191] 287.992035 222.951401 151.374817 177.977478 79.562271
## [6196] 121.789673 221.727509 165.717163 85.626244 173.270676
## [6201] 185.630325 159.214615 237.484695 255.634827 122.036728
## [6206] 273.223816 105.436310 211.258240 116.268967 147.351181
## [6211] 150.445251 139.047379 251.046066 162.492249 147.124420
## [6216] 111.284225 146.478775 105.607315 90.910263 89.004143
## [6221] 135.831482 117.689011 71.929543 163.747833 177.289749
## [6226] 217.378525 172.624603 154.019363 31.249817 257.987061
## [6231] 315.131226 113.805511 288.218628 104.702095 117.559845
## [6236] 96.605476 57.659180 187.360321 146.053268 144.368500
## [6241] 144.436340 248.785416 155.414841 233.428467 140.338989
## [6246] 158.564362 287.839539 175.608154 154.483398 250.234055
## [6251] 161.821579 184.874359 314.319397 218.420029 314.319489
## [6256] 213.589340 130.290390 149.737289 150.427994 151.869141
## [6261] 105.214279 105.380577 147.416214 264.842590 156.699036
## [6266] 173.498169 246.958328 112.005821 133.790359 183.051788
## [6271] 372.243164 231.629501 168.289581 183.404572 151.253906
## [6276] 112.854706 254.896591 114.936020 196.400925 152.075439
## [6281] 161.537292 62.208908 131.772858 240.228745 314.171661
## [6286] 113.342865 127.833519 156.442535 143.709579 127.975250
## [6291] 50.887447 116.929436 117.078392 188.720062 178.336807
## [6296] 100.040443 284.838501 126.516106 105.671158 157.254883
## [6301] 137.771683 487.660889 141.387955 152.343445 127.247169
## [6306] 214.381683 114.498657 34.220150 271.734436 107.225327
## [6311] 170.566269 193.583450 161.196198 176.156326 122.713699
## [6316] 213.584564 118.155838 100.600258 56.023033 134.852448
## [6321] 92.328087 210.170685 133.941910 100.476143 163.101288
## [6326] 178.863327 170.916428 149.766464 163.593719 180.441101
## [6331] 181.067108 120.192154 128.268478 150.040680 228.143784
## [6336] 166.234772 255.827393 134.284698 136.576035 166.377579
## [6341] 150.978699 197.209183 148.775864 186.538162 44.770771
## [6346] 134.663727 173.609329 118.886124 82.515884 164.390167
## [6351] 226.798248 167.710495 245.682205 178.981964 74.308861
## [6356] 195.828552 307.357819 165.330231 252.157761 296.951538
## [6361] 138.105637 201.025986 153.932510 176.986832 171.007690
## [6366] 177.801147 123.893860 167.677994 188.407272 128.102234
## [6371] 93.579048 196.132980 237.026581 148.375107 165.695282
## [6376] 241.302185 170.752609 196.071793 113.374527 508.824585
## [6381] 125.889832 152.939178 139.149109 162.474182 94.368729
## [6386] 126.585312 173.005753 142.765198 242.059158 233.844131
## [6391] 128.563538 207.811813 124.548607 242.329880 165.722046
## [6396] 178.483536 207.289749 241.083649 250.232529 184.972717
## [6401] 180.698975 232.434769 93.161285 60.599079 213.589355
## [6406] 115.981094 123.123566 191.817139 166.174240 243.938522
## [6411] 199.209885 160.269821 154.644348 111.011955 100.301971
## [6416] 134.376953 119.070053 170.375061 143.782089 148.352386
## [6421] 129.807144 157.365494 85.444199 135.231140 187.440796
## [6426] 174.223831 151.815140 143.549683 162.491348 134.757599
## [6431] 164.789520 135.234268 166.024460 108.507751 124.831627
## [6436] 157.624557 91.736145 141.962799 166.585785 195.048431
## [6441] 215.585876 129.342972 89.755424 104.653633 190.595337
## [6446] 129.046463 114.913483 75.251953 139.490280 267.668121
## [6451] 132.268829 103.519257 95.728088 52.233929 159.503387
## [6456] 144.874084 99.732544 128.010910 113.899498 125.674744
## [6461] 125.948540 81.299561 63.488277 145.600189 202.049820
## [6466] 82.317337 138.439606 73.570152 102.090363 76.944824
## [6471] 151.042786 72.063927 98.038620 104.711929 147.101898
## [6476] 121.396126 70.783325 213.425949 84.097580 84.329079
## [6481] 61.165989 154.360260 148.394028 102.603973 153.045349
## [6486] 116.918976 105.632591 70.175201 182.236450 88.177849
## [6491] 77.997978 140.990082 112.185562 144.960052 114.404434
## [6496] 105.559784 112.865021 177.848373 104.428383 65.750549
## [6501] 95.300896 98.428062 91.682190 82.209496 68.773132
## [6506] 108.648033 92.042786 99.324890 136.555862 68.895004
## [6511] 164.413147 89.534172 81.315582 129.448441 113.998756
## [6516] 72.494217 115.261597 81.305634 39.536411 99.636047
## [6521] 88.948410 90.243042 119.923744 85.111153 190.934875
## [6526] 245.092285 133.443466 115.498291 72.111900 59.259098
## [6531] 148.492264 166.601349 163.076889 89.025429 62.087536
## [6536] 142.021866 131.077164 92.366554 115.104141 100.287674
## [6541] 81.884171 69.243202 171.644745 116.401421 49.607521
## [6546] 118.918068 165.562424 167.420975 86.700912 89.836304
## [6551] 99.270920 178.979050 77.646416 155.459518 120.718964
## [6556] 169.625610 296.606293 103.822067 76.586617 114.913902
## [6561] 128.035675 220.999756 260.065094 223.546219 158.067215
## [6566] 298.152893 209.796158 164.716782 81.472694 280.629883
## [6571] 154.155685 164.246628 161.601608 181.071075 181.401779
## [6576] 127.089462 158.297363 161.348251 132.007462 119.339035
## [6581] 159.225021 80.923111 240.830414 73.107376 122.653458
## [6586] 213.879059 229.135132 228.213959 204.537628 99.879417
## [6591] 127.841110 224.012619 124.150070 85.632347 130.540527
## [6596] 153.088715 247.921906 165.148132 141.149155 143.544968
## [6601] 174.721848 102.364807 190.683243 192.664810 196.357681
## [6606] 135.466934 39.985062 70.085716 123.477524 189.880112
## [6611] 133.223373 299.375336 111.339783 196.155930 224.849274
## [6616] 225.542450 211.785431 229.218857 145.821686 210.206985
## [6621] 173.126633 134.009018 221.852310 117.201591 262.020416
## [6626] 188.258972 125.634689 204.706406 111.212257 229.163559
## [6631] 186.384430 238.903015 165.747238 93.501907 125.924149
## [6636] 172.852936 288.489471 183.560043 112.817947 203.205826
## [6641] 230.901794 149.601288 234.770248 166.498383 96.368790
## [6646] 148.591797 183.469040 142.302719 140.636612 132.851959
## [6651] 105.432030 122.769142 132.468369 280.535461 93.732582
## [6656] 185.109756 199.805573 155.570358 178.497635 222.813431
## [6661] 110.395386 175.721695 177.770935 111.945595 99.618202
## [6666] 55.487000 109.950882 139.478043 123.414680 94.550797
## [6671] 100.169884 76.502510 172.330872 94.146339 283.776855
## [6676] 124.971748 214.357727 175.820160 162.327759 128.559097
## [6681] 162.702621 121.980148 269.606720 83.666267 176.099976
## [6686] 135.851089 119.271423 135.608383 133.684158 217.361435
## [6691] 119.433411 153.140457 138.692352 60.191952 136.236465
## [6696] 143.917496 143.987793 300.252472 146.166336 159.483582
## [6701] 100.812469 130.979553 38.632679 117.227097 58.489620
## [6706] 127.267151 111.366707 132.699234 77.426048 96.362968
## [6711] 157.499374 112.353958 71.007645 144.869400 149.038284
## [6716] 95.402748 213.584946 122.804276 158.184143 128.489426
## [6721] 182.951340 118.739059 105.578064 146.449478 162.814423
## [6726] 112.347290 82.363846 83.324318 258.235260 142.372864
## [6731] 107.038193 134.423920 139.810913 102.193840 259.436615
## [6736] 322.372681 182.996826 72.931404 77.297401 174.897751
## [6741] 104.275635 132.201660 238.927765 90.467796 142.182953
## [6746] 163.860855 230.929138 202.575348 169.668594 152.931046
## [6751] 182.586349 92.199318 176.676025 208.330124 137.662247
## [6756] 208.918396 156.578354 90.786980 205.318024 79.333000
## [6761] 359.966125 138.159988 391.184326 161.957718 149.511841
## [6766] 160.240891 282.889252 285.220825 124.531845 164.865738
## [6771] 128.194687 138.028030 240.837784 242.640381 135.722382
## [6776] 203.645264 133.069443 142.445740 156.527466 117.579803
## [6781] 129.706223 125.305664 125.406860 131.118484 117.262344
## [6786] 319.412811 114.448677 113.376610 257.923523 101.729095
## [6791] 169.623474 114.399544 149.210236 205.958450 213.856827
## [6796] 223.196823 163.633606 163.838043 115.627098 125.483032
## [6801] 95.682823 185.182846 141.490173 84.064560 105.047966
## [6806] 83.206772 83.042938 111.485428 252.308594 131.136230
## [6811] 143.103958 247.940903 180.768936 94.437645 116.702377
## [6816] 115.953156 116.548073 207.407318 142.468170 114.274567
## [6821] 193.269684 240.065552 185.685638 138.000305 67.668144
## [6826] 139.029907 183.954514 141.682373 260.511719 90.420334
## [6831] 230.058731 105.935654 328.184967 126.823380 132.902985
## [6836] 234.006256 122.356003 262.400909 312.849426 79.833115
## [6841] 110.419052 80.049347 161.083847 100.493889 65.176636
## [6846] 194.152466 80.168076 131.955170 107.761154 181.166306
## [6851] 172.144424 77.831902 355.645264 125.003220 83.267105
## [6856] 57.404884 101.340431 70.542488 184.686310 277.843079
## [6861] 140.458084 104.469994 127.596199 66.832680 131.293411
## [6866] 99.249870 95.997627 84.210632 172.323502 158.825073
## [6871] 227.261292 254.524033 222.502548 122.602425 80.569489
## [6876] 97.882767 185.875015 116.344223 154.257782 155.427002
## [6881] 357.891602 70.994484 121.184525 102.081581 217.611450
## [6886] 92.998779 158.471786 102.693550 88.930069 80.297653
## [6891] 137.346832 94.569466 113.253647 106.274925 256.431763
## [6896] 207.840073 97.946213 127.176025 251.317490 184.703842
## [6901] 275.623230 145.340637 132.070023 86.338463 120.562698
## [6906] 116.759270 128.872742 200.869644 128.392822 126.772118
## [6911] 117.296181 113.925049 206.296341 122.925827 106.458412
## [6916] 172.218552 141.320404 137.411835 179.263046 123.106056
## [6921] 263.413177 189.139771 334.504456 101.823135 155.599792
## [6926] 256.839569 122.551704 129.377777 150.700165 156.364197
## [6931] 93.581596 114.944672 124.850006 92.458946 100.454514
## [6936] 20.255562 117.467049 176.752823 175.567413 129.737534
## [6941] 208.868851 138.988480 166.720123 65.493660 213.391876
## [6946] 121.906555 178.601715 138.089294 111.715294 106.743446
## [6951] 102.039230 122.880249 138.254425 147.392029 246.537201
## [6956] 182.071335 243.265259 157.116470 140.973846 85.069710
## [6961] 101.465126 94.724342 123.063698 98.186623 138.753113
## [6966] 85.056145 127.986954 112.586754 -5.389462 73.294357
## [6971] 71.626747 307.930267 183.026245 200.351257 132.219650
## [6976] 120.233978 145.714890 138.559067 155.222397 123.647705
## [6981] 94.316505 154.050690 97.478012 282.639587 86.159325
## [6986] 44.633450 111.184265 196.144882 120.684387 39.301018
## [6991] 97.974861 241.676743 233.138260 94.098145 89.354408
## [6996] 68.911606 142.138062 214.830978 216.019012 172.023849
## [7001] 65.012268 121.589134 194.895691 153.981537 81.999825
## [7006] 86.979958 187.231445 76.878387 106.361832 166.161530
## [7011] 194.258148 144.586349 209.279068 137.477371 163.787933
## [7016] 113.291611 121.112885 91.229050 110.158844 72.200989
## [7021] 66.415825 104.838791 85.588951 62.835682 117.925514
## [7026] 220.115509 86.070656 145.482346 108.414978 79.575287
## [7031] 182.706497 114.789673 213.739044 228.311798 180.118469
## [7036] 153.600357 108.065323 84.630836 131.858154 80.644676
## [7041] 251.516586 249.012558 124.205009 124.029938 95.103813
## [7046] 133.949219 137.476974 159.945419 116.050980 92.002190
## [7051] 125.178459 158.632980 103.781059 105.211014 104.422791
## [7056] 192.001968 289.808746 168.748734 139.910400 101.994644
## [7061] 120.134300 208.053772 95.108765 141.803802 177.786621
## [7066] 189.056381 107.889168 134.421478 99.390251 124.680664
## [7071] 154.081512 152.062256 88.163277 145.007767 205.011276
## [7076] 96.272812 150.235275 80.440414 68.282990 160.858414
## [7081] 275.715698 101.804634 192.931610 363.228546 59.711521
## [7086] 80.862923 94.861458 115.566803 136.189636 90.353203
## [7091] 86.356537 88.274582 362.194824 100.406120 127.061264
## [7096] 153.805161 100.405167 174.472427 145.645981 99.802826
## [7101] 184.069321 131.116150 78.359688 193.644135 130.494583
## [7106] 104.603111 101.744576 102.024391 104.722031 101.143768
## [7111] 102.956451 175.970612 82.411377 68.446205 133.739578
## [7116] 130.751465 127.006653 89.197792 262.053497 107.491043
## [7121] 313.701111 159.695877 177.825394 72.022087 103.060898
## [7126] 86.527863 221.957138 92.575294 165.113739 140.540314
## [7131] 235.572021 67.531052 219.331497 270.394440 126.963257
## [7136] 95.655502 69.876923 69.182899 175.935074 103.514969
## [7141] 171.367111 89.754211 99.946472 71.858696 121.954651
## [7146] 105.637466 67.531174 109.983650 85.759506 147.315872
## [7151] 107.971519 83.550774 579.494812 102.713013 61.663593
## [7156] 187.490540 249.039154 96.559448 137.617996 76.870636
## [7161] 86.171387 137.283676 61.848438 70.793289 114.346466
## [7166] 111.595482 118.229797 91.133209 133.736923 203.604019
## [7171] 121.538185 94.746544 166.625336 136.418167 87.296257
## [7176] 91.065025 221.177750 265.828522 145.106735 79.646492
## [7181] 116.181549 89.247322 138.861710 120.449371 90.381859
## [7186] 114.431259 92.133446 279.817780 139.822052 203.569931
## [7191] 81.585754 109.520775 130.864075 99.883247 113.697975
## [7196] 102.033119 96.880066 106.413628 160.046005 90.984734
## [7201] 113.950180 137.742828 122.745621 169.209259 224.375824
## [7206] 201.763229 184.701294 66.040642 186.157944 164.861893
## [7211] 121.573891 168.583344 173.497406 97.383423 137.116745
## [7216] 124.717636 136.329590 124.502472 115.083023 166.732880
## [7221] 264.474030 134.913300 193.784988 128.281906 79.813644
## [7226] 271.372345 124.249733 240.770844 143.440903 87.874702
## [7231] 68.956703 92.382095 83.625496 97.599091 100.992065
## [7236] 97.159081 254.430252 159.937149 111.964622 84.188774
## [7241] 206.004303 191.350952 129.321854 85.615158 129.033676
## [7246] 164.121338 72.049934 127.256287 102.713409 140.247971
## [7251] 107.668770 69.064445 105.102859 124.071976 153.951126
## [7256] 115.728043 77.741043 240.009781 195.176407 234.689087
## [7261] 304.917114 112.327011 214.140533 280.802002 99.486794
## [7266] 192.721954 71.137657 156.932037 175.299576 76.239235
## [7271] 144.856934 130.579956 194.636185 82.012505 109.876160
## [7276] 37.334713 136.225769 204.100113 89.672455 130.883194
## [7281] 123.855415 166.032715 102.364868 71.582703 99.183365
## [7286] 66.656441 146.111603 93.070839 169.440125 150.016525
## [7291] 328.087799 139.597290 119.026810 200.178070 119.071388
## [7296] 135.773102 218.580460 112.022522 100.220329 266.949463
## [7301] 129.000702 371.118103 136.602570 174.532623 142.002029
## [7306] 175.997040 120.128708 58.914371 100.651611 219.077774
## [7311] 226.134201 138.076767 121.059586 192.133057 54.351360
## [7316] 191.739563 82.844872 132.766693 110.617020 121.238983
## [7321] 99.963799 107.397232 185.641510 95.141434 180.460922
## [7326] 106.474815 104.452942 131.929947 154.837662 184.608032
## [7331] 123.085121 53.284267 122.638977 220.093796 148.997620
## [7336] 131.887314 123.039474 186.766876 152.577820 126.360565
## [7341] 146.641922 155.076202 293.405579 68.676788 93.604263
## [7346] 277.981384 201.497604 137.196899 90.984535 189.044098
## [7351] 137.421707 116.415359 103.848022 362.084351 111.206200
## [7356] 149.371246 59.306305 70.984489 161.040985 218.788864
## [7361] 203.798523 66.096779 122.435295 133.677979 116.225113
## [7366] 106.390945 159.865784 93.566223 257.664154 96.493011
## [7371] 96.691628 137.382996 106.811249 154.013809 236.783203
## [7376] 83.517998 119.706665 141.146591 78.280838 128.157227
## [7381] 188.794281 75.220901 263.386475 144.832031 105.855827
## [7386] 180.434708 247.209015 181.370697 207.745361 150.766525
## [7391] 119.504669 111.037498 148.741562 101.306450 172.451584
## [7396] 56.387924 350.639435 135.204391 126.294113 161.380249
## [7401] 197.947266 87.469414 90.420074 90.968201 168.684875
## [7406] 99.375732 163.277878 235.719101 94.372482 146.068420
## [7411] 124.538139 193.039230 171.058655 162.155029 139.149811
## [7416] 123.980446 242.553360 95.459755 106.411514 134.654541
## [7421] 129.235596 112.855858 162.224548 121.225067 -62.705025
## [7426] 364.423309 128.099106 148.297455 86.690460 112.282234
## [7431] 47.176521 131.260223 96.407944 149.574600 168.500854
## [7436] 192.578979 128.404449 110.873161 132.096069 221.509842
## [7441] 135.139008 138.240509 146.985901 97.489609 110.140465
## [7446] 70.547363 98.053162 110.007042 153.112411 157.778793
## [7451] 258.063202 141.679825 134.392029 117.888741 102.788254
## [7456] 163.694855 114.426048 73.601295 142.313995 318.131622
## [7461] 110.684097 206.595169 89.801804 329.438507 128.153595
## [7466] 217.247299 94.412964 86.188454 86.770546 130.758591
## [7471] 117.191719 109.932068 71.163239 118.990433 94.884651
## [7476] 13.817130 168.820145 59.849056 117.959137 111.791283
## [7481] 150.209259 103.466591 133.909805 71.125832 166.216507
## [7486] 125.151642 310.662354 91.390045 106.673492 -47.908173
## [7491] 122.255310 -27.276272 165.942200 107.195412 109.775421
## [7496] -23.833965 150.424805 62.537983 131.014221 -31.891361
## [7501] 101.725693 84.198395 115.847336 63.535099 77.747963
## [7506] -31.873150 159.256592 112.478157 85.658081 -40.333614
## [7511] 111.731438 77.645569 234.070633 192.059418 61.822769
## [7516] 227.965210 113.113060 164.251419 131.914413 102.790916
## [7521] 136.702408 48.199326 111.000717 103.262466 111.640427
## [7526] 96.759872 119.383354 108.601692 133.009232 119.408241
## [7531] 90.855804 203.255768 148.789764 92.862579 123.711502
## [7536] 117.817741 74.518204 97.595276 120.422279 83.496948
## [7541] 45.406761 134.433426 106.937271 196.820526 204.925964
## [7546] 85.410576 194.514664 258.866791 137.381149 175.870667
## [7551] 229.404678 159.459244 111.332687 152.940735 302.855438
## [7556] 106.115906 97.246788 255.872726 163.520630 100.570084
## [7561] 76.259041 86.798256 127.810501 116.683624 215.739532
## [7566] 296.078125 113.131287 119.769707 137.162964 54.445015
## [7571] 81.747833 161.174805 85.128647 123.388657 93.692276
## [7576] 125.349625 262.719971 96.425133 330.342804 68.873390
## [7581] 114.238350 89.501122 185.762680 125.522743 173.597672
## [7586] 123.747292 158.594574 159.558151 167.952362 85.829369
## [7591] 83.172256 431.171936 116.714951 73.855293 246.836044
## [7596] 113.819229 79.485298 62.187477 176.853210 92.819221
## [7601] 170.759521 134.536224 171.063828 164.466705 60.955082
## [7606] 246.581863 127.575745 79.356468 107.617912 121.662590
## [7611] 84.716988 160.843262 278.531769 209.704529 271.115875
## [7616] 105.818085 120.408051 163.020020 140.652100 252.628159
## [7621] 170.338943 169.406067 110.097084 228.645721 159.360138
## [7626] 60.104824 214.035645 350.111542 83.988441 192.108139
## [7631] 204.832443 112.003754 236.949646 82.570786 277.182312
## [7636] 97.453300 265.407379 74.270721 195.069107 98.782776
## [7641] 118.623352 166.497147 184.782684 221.303802 149.654495
## [7646] 296.475281 90.275085 106.244865 175.299973 173.721649
## [7651] 119.451836 301.570312 260.701691 226.857224 104.875656
## [7656] 260.992279 196.793777 132.908432 91.908585 135.725647
## [7661] 137.483170 216.771927 140.589706 233.965714 72.235695
## [7666] 251.793396 150.988571 89.001297 107.711548 179.237030
## [7671] 173.283630 144.002594 72.784592 87.182274 146.547470
## [7676] 112.762169 218.778915 243.273712 258.888458 208.881378
## [7681] 260.020233 136.303177 422.450073 267.493164 142.012146
## [7686] 266.549286 325.871613 141.235947 175.996384 108.790222
## [7691] 334.058472 196.253586 235.836029 118.530548 239.413818
## [7696] 260.025360 201.196716 277.814697 142.193359 108.133728
## [7701] 243.514496 127.896088 86.402779 449.974854 196.336792
## [7706] 314.638763 147.880630 187.107437 259.231140 196.958115
## [7711] 261.287781 294.580109 278.204132 121.694893 196.447571
## [7716] 273.350342 240.057953 109.561684 86.919319 88.459946
## [7721] 66.390045 214.453125 144.558487 93.834442 208.761429
## [7726] 113.556206 126.621628 116.675476 110.259628 504.376099
## [7731] 100.329956 58.337811 171.808701 124.792297 78.735764
## [7736] 195.717422 125.993317 240.431671 81.973145 84.369354
## [7741] 143.800308 213.465134 136.270325 114.219322 133.776657
## [7746] 242.552505 285.220184 152.785446 230.154266 127.435944
## [7751] 149.418259 170.799179 92.811028 203.518890 86.110397
## [7756] 121.646492 108.351494 164.892181 146.000702 98.220215
## [7761] 143.433899 298.780243 111.557571 135.296188 179.819992
## [7766] 72.133301 158.176880 117.237564 141.017273 118.890808
## [7771] 227.310349 228.344101 40.460583 89.263802 152.297455
## [7776] 134.384384 85.894730 104.282135 176.399170 101.775574
## [7781] 110.841286 155.225266 114.970619 187.433029 115.235298
## [7786] 135.335083 90.737091 114.677689 336.526123 60.604668
## [7791] 157.107819 193.379959 171.721130 202.321899 166.008881
## [7796] 178.506775 136.107437 171.061920 162.700684 301.123993
## [7801] 131.419159 234.684692 96.531662 78.913147 244.404053
## [7806] 138.056198 245.567902 170.031525 88.404228 87.737862
## [7811] 133.421127 293.178223 205.363266 145.294846 144.491837
## [7816] 115.490662 137.084366 84.967232 160.995605 313.260559
## [7821] 130.550095 89.529373 150.709839 77.684036 180.718964
## [7826] 198.103897 154.941986 84.010597 156.679840 155.798630
## [7831] 113.054520 187.390106 128.510315 134.882812 123.981972
## [7836] 144.707306 154.892548 179.460648 155.948334 115.908478
## [7841] 142.274490 132.427429 144.559769 155.010483 164.434372
## [7846] 119.884552 108.250671 159.051178 109.626099 232.701981
## [7851] 124.114670 170.657104 142.398148 148.317337 174.597809
## [7856] 163.763199 123.764099 143.959167 120.166183 128.730453
## [7861] 197.029800 71.813004 172.003815 136.461426 156.257019
## [7866] 255.739777 111.732559 88.287369 94.555328 121.256256
## [7871] 157.208542 148.149353 162.145630 118.091858 81.128441
## [7876] 105.296555 164.325607 97.757065 154.568985 182.276505
## [7881] 175.702835 94.168068 124.780457 93.586143 90.026367
## [7886] 132.142578 251.217880 102.867371 143.650757 142.038666
## [7891] 151.285309 151.212708 120.307907 118.396255 129.595627
## [7896] 118.265564 374.230438 188.903030 149.585831 143.413513
## [7901] 177.596207 209.041809 122.888412 77.640106 145.890366
## [7906] 144.389587 258.004364 89.363373 123.942398 190.299133
## [7911] 168.334442 158.477875 146.520935 131.541458 104.204605
## [7916] 142.358597 145.291870 131.855316 140.020874 97.469299
## [7921] 119.069504 191.005508 81.299088 159.985458 146.163513
## [7926] 122.684761 141.851974 159.572479 136.619217 177.410797
## [7931] 117.618729 131.650803 169.856628 241.707809 96.129318
## [7936] 111.350327 87.870964 108.910347 145.227707 187.260513
## [7941] 139.693192 136.506165 170.348816 214.017288 215.145752
## [7946] 114.669525 173.240021 108.479347 93.206650 110.136276
## [7951] 120.097023 134.095551 203.189651 86.649460 130.053680
## [7956] 136.961746 34.908531 128.857468 72.962524 34.251427
## [7961] 76.398216 165.554535 197.918167 131.982010 114.347275
## [7966] 132.732391 113.295830 133.107559 182.934952 140.896027
## [7971] 112.680252 264.506927 84.221268 285.052734 58.136864
## [7976] 107.591461 204.243591 174.410583 218.046555 167.496872
## [7981] 112.997238 105.717880 132.707733 53.506329 91.835403
## [7986] 118.669426 154.445358 131.675735 112.746857 119.140564
## [7991] 134.004517 201.562592 65.935921 158.352921 198.185028
## [7996] 224.050400 127.688332 209.758865 46.281025 138.238800
## [8001] 97.406227 153.616165 131.918060 248.583984 125.248108
## [8006] 130.411667 171.562668 237.277573 65.323868 158.539459
## [8011] 181.941177 71.781906 259.944122 167.658859 109.454422
## [8016] 227.620773 145.059265 210.303604 91.674423 202.094452
## [8021] 209.538559 204.544357 147.761749 176.636292 163.396561
## [8026] 232.321671 163.424408 232.908447 195.792465 217.337997
## [8031] 142.864716 119.444046 116.188553 89.436562 117.990646
## [8036] 127.629372 178.741989 292.027191 157.264557 151.534180
## [8041] 78.119423 85.223511 136.313080 135.492310 150.642670
## [8046] 262.199371 178.988266 123.230957 259.202393 256.754761
## [8051] 133.414581 134.607590 301.084625 141.751526 155.095139
## [8056] 119.033951 132.851608 66.519180 127.306610 127.036415
## [8061] 116.025513 236.211548 80.122948 114.048248 132.048035
## [8066] 89.308449 233.077545 141.648392 159.197388 244.705093
## [8071] 188.748993 165.455383 111.856651 99.111298 114.116684
## [8076] 127.379242 114.980789 158.551300 135.596527 124.281128
## [8081] 177.325775 131.374771 118.925453 164.038788 141.445267
## [8086] 150.212524 124.510223 166.669250 90.919052 245.778503
## [8091] 228.775421 177.220535 156.332626 254.089081 168.649490
## [8096] 100.559441 172.320755 120.338852 117.147003 110.950912
## [8101] 269.577240 123.241142 210.243042 142.165619 128.479782
## [8106] 103.158783 214.037949 258.518707 191.623535 169.229675
## [8111] 152.289917 123.595779 112.896805 152.442291 176.253387
## [8116] 101.194046 178.446442 162.285309 147.090714 124.675964
## [8121] 152.783417 54.961632 103.681519 114.140686 102.907463
## [8126] 104.608055 248.292419 115.303284 71.507309 221.065262
## [8131] 226.593063 141.732498 58.713657 88.062408 106.067230
## [8136] 181.073166 320.100952 82.236099 163.909698 236.871017
## [8141] 110.797508 131.507828 83.791901 149.605988 141.706970
## [8146] 345.745911 101.905533 188.199295 114.883255 287.516632
## [8151] 113.096115 167.407684 117.301750 68.716644 115.463348
## [8156] 85.641014 98.504044 148.817123 177.917633 77.626656
## [8161] 132.996246 207.775360 104.965797 202.925842 111.890892
## [8166] 66.155457 141.862503 173.174286 153.421616 314.642273
## [8171] 86.814522 120.464073 121.298340 130.892578 96.762054
## [8176] 275.530701 223.234268 181.874405 176.625168 240.505814
## [8181] 115.289551 122.684296 283.483734 64.922783 73.631416
## [8186] 59.288101 108.197639 78.380745 91.989174 140.962143
## [8191] 232.843307 221.463470 197.528030 118.133072 201.349991
## [8196] 112.352829 112.720375 199.425186 258.724609 223.521851
## [8201] 73.873627 160.374115 88.259232 90.777031 164.436203
## [8206] 161.897217 90.752281 147.083160 82.883423 80.728951
## [8211] 70.136238 129.394699 190.369873 89.477913 140.822495
## [8216] 107.870850 73.727257 292.680023 40.811531 100.113724
## [8221] 197.136108 276.781372 171.586136 115.300537 137.603989
## [8226] 205.544922 109.238792 216.307388 462.340790 127.309967
## [8231] 334.597595 58.474472 123.246521 88.838562 119.693436
## [8236] 143.357605 127.234329 130.552063 129.363998 33.739700
## [8241] 100.274055 113.660919 144.648422 131.237076 214.226532
## [8246] 105.368378 106.523834 169.623154 122.394714 150.369598
## [8251] 78.585663 108.801819 397.853241 101.088730 104.780281
## [8256] 115.422508 105.898003 76.884491 246.480865 92.890923
## [8261] 113.229324 161.778275 276.384583 80.830299 135.271652
## [8266] 169.374268 174.449997 228.385315 91.289291 508.692566
## [8271] 201.279053 73.638062 136.136292 181.846268 201.474609
## [8276] 111.699883 187.411148 196.627304 149.552994 183.268921
## [8281] 179.702820 103.970192 166.202866 66.544174 234.331772
## [8286] 81.127129 107.404129 117.865631 127.897949 266.587769
## [8291] 111.843765 124.385307 116.662811 138.101212 152.073303
## [8296] 58.974834 53.089928 101.433975 138.893539 59.977352
## [8301] 276.243042 147.278778 129.482254 209.778046 250.666519
## [8306] 99.919357 97.025673 171.827942 173.393341 110.692833
## [8311] 269.412292 104.132469 125.116333 133.454285 234.475449
## [8316] 133.114807 134.143967 241.684891 123.002380 159.661087
## [8321] 202.875778 130.824051 93.307816 264.791351 200.376556
## [8326] 216.310715 154.523392 116.917953 159.945679 186.751404
## [8331] 179.701035 258.164124 150.970230 141.809998 92.950882
## [8336] 112.168633 116.431335 176.405228 107.033981 177.461716
## [8341] 118.032776 114.868011 222.788757 279.095032 101.379700
## [8346] 120.830292 63.259624 193.945251 96.399078 107.054245
## [8351] 139.383804 140.499634 137.600220 69.858307 125.205299
## [8356] 326.205322 186.524048 136.759109 147.961777 186.328552
## [8361] 287.302948 80.570724 185.015228 162.240036 133.419693
## [8366] 147.195648 159.667511 158.003769 157.255478 114.870323
## [8371] 54.851028 235.060562 292.881592 127.018539 122.211899
## [8376] 79.607315 324.827423 176.049393 236.005569 128.952545
## [8381] 162.638901 84.089546 311.039948 239.758392 182.024445
## [8386] 171.299240 173.104691 182.605988 285.737640 101.518425
## [8391] 199.414932 183.679062 83.247444 106.639198 150.078430
## [8396] 131.897919 174.527573 227.255524 140.266998 69.244568
## [8401] 360.106995 110.152756 323.202087 196.050079 169.910065
## [8406] 67.097557 108.229317 181.902863 129.140900 197.328751
## [8411] 115.897873 182.130936 228.541031 49.825920 105.220543
## [8416] 161.928680 110.418976 113.560158 114.810989 110.809357
## [8421] 154.459671 120.433807 97.868866 85.645164 155.478561
## [8426] 145.082199 84.502823 90.661873 94.042221 259.238678
## [8431] 60.313995 188.056442 223.257233 107.947495 148.131546
## [8436] 132.446991 58.083206 149.097961 178.482590 74.069809
## [8441] 115.102463 95.493607 52.932003 154.043396 255.501358
## [8446] 141.953476 169.610733 81.970047 199.043320 88.343399
## [8451] 116.112732 228.613953 26.753235 127.120781 46.336418
## [8456] 82.400360 362.030457 240.402985 190.721695 151.761002
## [8461] 86.015587 87.516777 253.576691 156.725845 308.430176
## [8466] 140.405273 235.269836 95.383598 69.561111 125.120476
## [8471] 149.670319 106.777840 71.530907 139.252594 190.236908
## [8476] 40.840023 135.059952 104.310349 66.016800 186.554779
## [8481] 135.159958 119.119949 174.645340 276.134583 100.240822
## [8486] 116.917221 131.306931 149.161163 63.907452 123.813530
## [8491] 104.791145 187.148956 125.605911 187.708862 111.141159
## [8496] 89.892342 85.919395 120.027710 234.417664 158.619400
## [8501] 153.882584 130.999115 137.406036 123.759537 229.855331
## [8506] 137.064392 158.331528 147.451004 163.420425 246.017609
## [8511] 170.229767 87.226837 106.928658 184.732727 111.395111
## [8516] 162.414703 72.439964 130.140793 92.752480 219.694626
## [8521] 162.677780 146.973083 145.655441 97.324806 174.338791
## [8526] 147.871674 85.992020 115.359894 127.598221 93.444725
## [8531] 124.063309 113.019043 -2657.951660 161.527618 157.506454
## [8536] 196.233398 110.099998 134.656113 170.377609 145.233658
## [8541] 152.820007 217.643372 107.468582 279.044128 102.661682
## [8546] 194.151138 201.717346 47.545532 50.365189 81.689720
## [8551] 186.358856 207.418533 216.252945 104.269379 180.869492
## [8556] 97.855080 151.765594 84.491272 115.847931 94.729385
## [8561] 286.918365 101.097656 118.180267 269.513245 103.978439
## [8566] 89.602547 136.322510 176.426620 169.356094 161.488205
## [8571] 160.293396 148.804993 110.556053 125.929947 114.462494
## [8576] 87.870605 129.711960 82.950104 186.367615 92.355446
## [8581] 64.961105 167.870483 107.306488 46.433430 257.903687
## [8586] 134.911667 112.490715 82.714851 109.356415 72.935944
## [8591] 86.305481 110.615654 107.112320 201.879623 159.275681
## [8596] 101.809143 153.337311 87.932480 153.575684 92.816322
## [8601] 77.615814 133.189697 128.886688 113.506119 160.132828
## [8606] 91.811897 150.505417 96.000938 158.354202 140.601013
## [8611] 178.163712 86.638802 334.467529 103.757530 170.470444
## [8616] 115.866821 131.590469 117.862236 111.733559 136.601105
## [8621] 106.067558 107.002823 156.261734 133.908905 105.574661
## [8626] 146.739700 83.500824 139.160980 117.424492 98.574989
## [8631] 174.785309 118.719025 111.479691 120.825348 192.756927
## [8636] 114.975502 92.263123 228.189316 183.934235 107.659180
## [8641] 60.267479 96.192886 109.290398 79.585976 83.064438
## [8646] 104.378357 71.785355 71.064095 99.783875 110.632965
## [8651] 233.134659 108.412697 123.386482 99.374680 110.369164
## [8656] 116.080368 104.322929 124.605957 103.842239 287.661530
## [8661] 155.252151 88.446541 136.274872 112.012665 87.710182
## [8666] 140.411896 153.595413 174.955139 114.234222 204.783310
## [8671] 62.011227 110.234932 75.057060 142.742874 112.088791
## [8676] 123.548264 425.226135 116.031021 191.894028 144.513550
## [8681] 192.309967 137.686600 283.293182 113.123550 109.756424
## [8686] 110.680511 62.804493 141.940750 96.793701 185.295120
## [8691] 124.114952 86.648964 83.511307 102.430168 75.551613
## [8696] 72.322411 115.595879 195.273392 109.714943 136.019257
## [8701] 103.222656 59.749176 101.799713 72.442619 134.590576
## [8706] 288.675446 64.715202 106.719658 210.969894 91.892967
## [8711] 119.933243 187.687134 98.014984 125.852509 123.833229
## [8716] 174.175232 132.498459 90.265450 218.118652 131.761047
## [8721] 312.480103 154.332962 108.817230 78.603935 62.144650
## [8726] 82.679001 117.833969 121.315628 115.586456 142.446365
## [8731] 91.378639 162.872498 115.115143 96.713219 72.388062
## [8736] 162.996124 93.865479 122.135353 154.897202 124.437523
## [8741] 104.896973 101.964600 119.639336 229.811081 123.284966
## [8746] 142.399338 180.782242 190.055725 106.773727 163.689926
## [8751] 125.971016 105.284370 67.168205 155.972656 82.991447
## [8756] 201.278183 155.968872 65.593292 144.143646 127.018440
## [8761] 327.621063 70.530121 141.884766 137.712662 185.377335
## [8766] 110.057732 167.654953 151.941605 113.893280 100.482819
## [8771] 125.043198 159.408691 519.095642 121.131485 103.560875
## [8776] 131.679642 155.511169 93.565201 160.343796 138.287338
## [8781] 155.103363 154.074371 448.165070 272.822205 139.078781
## [8786] 184.321030 105.930016 113.053864 91.860191 165.744400
## [8791] 191.172165 92.912109 142.670349 105.916473 155.235672
## [8796] 103.804344 116.111130 99.312256 183.497681 155.809097
## [8801] 104.401253 108.319580 89.235184 275.940704 129.518585
## [8806] 184.087784 143.198944 196.010376 90.278915 151.468369
## [8811] 108.141045 96.847778 126.356133 145.266953 124.168739
## [8816] 139.891998 350.617218 224.219437 174.985931 204.789383
## [8821] 158.751495 137.957672 187.289841 158.396332 123.854538
## [8826] 81.488579 155.809647 112.122093 177.828323 98.227097
## [8831] 155.138519 133.765045 89.926125 395.433167 116.658157
## [8836] 174.650772 122.537651 137.132614 259.386871 141.529282
## [8841] 104.704727 196.361084 144.958649 95.752914 238.500595
## [8846] 249.078445 108.498604 109.037498 163.198074 130.035721
## [8851] 133.985779 127.622978 107.074707 101.129036 114.044212
## [8856] 141.714127 142.738602 134.709564 158.823578 123.549057
## [8861] 118.643631 120.683945 119.158737 166.712234 134.883408
## [8866] 143.297394 294.777344 92.500893 261.060242 151.653671
## [8871] 65.049911 128.635773 120.811310 92.337448 149.658508
## [8876] 129.946106 129.437454 139.836670 145.697128 217.118317
## [8881] 164.711807 162.033127 139.147644 115.593643 155.514130
## [8886] 119.157959 116.477905 169.119263 144.834198 161.486649
## [8891] 203.229752 169.935287 186.974350 180.041382 237.727966
## [8896] 117.220642 148.778687 167.644287 140.451279 259.594910
## [8901] 147.157089 219.279449 95.639900 191.926788 101.493683
## [8906] 152.835983 118.740273 162.332108 113.259377 310.769775
## [8911] 114.235077 96.286087 153.591248 89.029968 140.179153
## [8916] 101.896347 96.062714 72.690628 111.835457 148.943970
## [8921] 120.417854 92.326103 126.146095 100.961998 257.080536
## [8926] 269.577759 103.016258 114.332016 93.359520 95.126900
## [8931] 196.520889 62.684719 118.938164 72.062050 117.723381
## [8936] 86.553139 159.716858 93.644043 97.793777 94.354790
## [8941] 164.085083 127.429039 120.190620 150.690536 225.606186
## [8946] 82.359291 174.162399 153.835083 99.493156 128.108368
## [8951] 180.322647 155.951691 82.074821 138.943253 89.786705
## [8956] 145.537872 110.660980 126.580307 137.372192 97.902061
## [8961] 98.379501 251.269226 124.217041 87.373787 103.777649
## [8966] 145.762070 122.556122 163.841766 134.815582 135.818237
## [8971] 186.953796 223.759506 55.794098 201.428101 106.808998
## [8976] 70.793404 136.472000 113.450897 101.607765 103.258492
## [8981] 214.924438 173.611771 126.590660 78.357063 248.659409
## [8986] 127.296936 190.063477 180.266769 119.508942 65.662598
## [8991] 76.016716 103.736519 78.403557 91.155365 208.557556
## [8996] 112.132072 102.533279 178.710037 93.037079 162.941055
## [9001] 140.157333 77.224869 201.526794 120.275902 107.024956
## [9006] 137.415543 81.842903 133.232224 110.685974 149.791306
## [9011] 185.432358 94.341667 125.157944 113.771217 107.307617
## [9016] 117.996239 153.197540 140.735352 157.054382 291.630646
## [9021] 94.063957 154.862228 201.594604 202.040497 133.739685
## [9026] 110.897797 81.094688 109.565796 111.136810 168.109299
## [9031] 150.235138 165.492508 192.782089 204.585419 170.396317
## [9036] 96.105103 334.325500 80.942200 145.346115 107.789108
## [9041] 82.969643 128.449326 144.836029 73.511330 146.809433
## [9046] 61.358856 124.891937 170.967422 136.583084 189.712448
## [9051] 129.417358 63.731106 117.456940 59.841587 136.481354
## [9056] 82.149071 68.113213 123.989334 111.629181 124.047966
## [9061] 83.238480 101.398216 188.989655 135.800522 134.914764
## [9066] 138.303146 172.152603 144.752136 90.181923 120.558914
## [9071] 232.473404 62.525124 114.871620 152.940628 153.040970
## [9076] 87.696518 93.663673 100.198997 167.698792 137.359161
## [9081] 173.730652 198.038803 86.856339 105.234413 103.179710
## [9086] 98.063812 104.013252 172.844986 69.472153 202.878448
## [9091] 116.769623 107.121864 134.195404 211.799805 148.547485
## [9096] 116.189056 114.245125 156.398163 73.736916 264.329437
## [9101] 180.021591 110.969475 202.758423 163.842484 103.038338
## [9106] 142.245239 240.099365 120.140411 181.289154 216.367920
## [9111] 140.573624 188.695618 217.860001 109.473083 161.561218
## [9116] 133.688782 103.811981 163.908356 88.657707 91.640396
## [9121] 104.877304 147.022980 117.471130 274.716492 89.146103
## [9126] 129.022949 130.533096 110.522812 153.616440 140.368866
## [9131] 221.406693 63.828037 129.857697 109.915192 62.084724
## [9136] 113.520615 211.589233 160.884262 173.227356 111.727119
## [9141] 113.251190 189.548691 294.404358 233.345352 178.235168
## [9146] 105.896080 147.209518 140.521805 121.323822 117.539665
## [9151] 120.954437 95.032715 148.020844 116.543182 147.340698
## [9156] 91.647278 174.061325 103.768097 123.677437 99.729889
## [9161] 161.364258 188.818695 174.639191 292.948517 107.870926
## [9166] 105.076889 146.472595 118.577858 132.667160 259.757935
## [9171] 75.817207 118.726578 134.873749 93.214310 145.411072
## [9176] 97.533485 184.071762 72.488358 115.381958 156.816071
## [9181] 140.975571 68.286423 123.774475 101.744186 153.129959
## [9186] 140.051102 171.603989 101.906013 83.654793 90.842796
## [9191] 92.954239 127.511322 176.256790 77.419144 64.827591
## [9196] 121.331619 104.028999 137.100281 177.293671 90.682922
## [9201] 192.408722 361.605011 198.273529 111.368553 105.138916
## [9206] 124.289719 75.338852 74.890152 205.167236 132.467560
## [9211] 107.189728 113.088753 123.874542 201.081528 100.228035
## [9216] 179.286667 171.010727 208.675873 153.828934 198.476624
## [9221] 113.416733 141.038193 215.722198 112.849716 102.891533
## [9226] 73.264587 215.034729 203.375809 74.462959 104.637383
## [9231] 164.580811 74.553673 84.912560 317.723999 150.079956
## [9236] 111.952225 117.141403 106.028244 83.943581 99.470894
## [9241] 165.951782 101.749451 226.645111 139.074905 154.717896
## [9246] 74.653160 116.749695 209.494431 143.637939 201.887405
## [9251] 86.751740 78.460793 105.786469 108.133820 96.745094
## [9256] 229.567139 187.187119 141.086990 82.644859 112.876129
## [9261] 134.123260 103.599152 196.791443 121.505760 103.773888
## [9266] 142.884064 206.853241 74.136246 173.616348 179.071594
## [9271] 94.809219 121.098579 292.401855 136.049072 98.300362
## [9276] 174.725357 145.626770 113.290009 127.901184 115.328659
## [9281] 183.125412 95.547897 89.056740 150.908829 94.336128
## [9286] 178.684402 111.280098 143.078033 107.828964 194.941284
## [9291] 110.577713 269.726471 131.887268 94.991127 183.600464
## [9296] 110.148819 124.406158 128.528519 179.003006 163.587204
## [9301] 151.801788 237.799316 163.769455 138.356537 117.224983
## [9306] 83.735146 116.531372 77.008553 195.391418 128.876022
## [9311] 115.956200 105.692886 254.584290 121.168167 162.277008
## [9316] 208.748947 84.871979 260.307648 183.982452 147.919952
## [9321] 174.320251 123.070366 122.590790 100.140625 169.667862
## [9326] 148.533951 114.442352 124.535873 83.128319 53.356033
## [9331] 103.143044 52.461239 70.643585 146.272003 98.236908
## [9336] 148.538193 141.254288 107.449379 122.760643 116.022331
## [9341] 53.653236 159.582016 67.502602 127.020134 159.486481
## [9346] 86.255295 130.614578 224.567276 93.601913 130.224762
## [9351] 143.252197 121.177284 118.616653 112.735191 93.468887
## [9356] 109.453522 113.380737 72.465996 55.218575 207.968002
## [9361] 104.013657 95.283432 157.351364 105.337708 217.687668
## [9366] 147.460373 131.122986 497.960724 104.629639 302.772949
## [9371] 156.086472 109.801849 180.476913 177.952286 146.348831
## [9376] 61.451313 111.192368 140.257812 160.372925 118.252220
## [9381] 91.551117 217.694504 103.197403 104.785683 121.100784
## [9386] 135.946671 148.634308 123.807480 121.055862 83.009979
## [9391] 91.946892 156.768372 94.606644 86.066719 60.053822
## [9396] 192.980469 80.108978 201.269455 114.034195 215.603714
## [9401] 121.041283 88.244789 135.299942 170.703323 219.242966
## [9406] 116.572685 76.588287 173.336487 158.747055 104.901596
## [9411] 121.776283 66.344231 231.455994 125.970230 105.319984
## [9416] 64.371429 109.954277 69.011482 146.661758 84.764954
## [9421] 190.974182 119.871384 85.735611 95.881378 152.186081
## [9426] 66.141838 199.027054 106.570412 107.526779 116.862839
## [9431] 41.930859 105.844353 124.397339 133.258087 81.137962
## [9436] 157.433670 220.759277 86.916252 95.885361 160.148224
## [9441] 114.488052 105.839233 112.110535 104.729065 83.617279
## [9446] 97.312744 205.280975 74.381821 46.978237 101.778786
## [9451] 192.894287 176.018921 167.212799 83.049240 128.888580
## [9456] 90.650169 100.045769 175.925476 94.490906 71.727570
## [9461] 83.120590 126.067879 94.842697 135.814484 153.952637
## [9466] 137.965149 146.614059 178.761047 69.908607 276.263580
## [9471] 135.514954 143.989868 147.050522 121.656105 123.547653
## [9476] 126.048866 75.541656 123.232086 208.445679 105.872879
## [9481] 85.731461 113.532303 103.660736 215.357452 163.804291
## [9486] 115.481934 128.748917 123.640114 81.751175 105.659599
## [9491] 198.439117 80.627090 105.142014 80.350372 76.169281
## [9496] 158.042542 105.889908 152.424393 189.096664 133.118698
## [9501] 262.875519 146.944229 69.360832 118.504578 94.624550
## [9506] 115.966934 148.154541 124.094955 118.321014 138.665634
## [9511] 141.645996 122.750793 131.845627 160.978729 248.892639
## [9516] 186.028290 164.611893 185.811661 111.278740 85.462166
## [9521] 209.953461 72.234283 140.433014 87.220863 152.487747
## [9526] 213.223419 107.974808 101.540802 360.350494 84.147232
## [9531] 96.413055 89.662224 142.671783 73.883224 101.071465
## [9536] 112.626900 206.380951 229.742676 137.163208 175.514435
## [9541] 185.073410 273.253693 151.520859 110.536636 196.656998
## [9546] 125.024017 127.590668 70.070107 111.071350 507.913483
## [9551] 210.053162 98.731567 109.355179 138.423630 123.450943
## [9556] 115.774048 100.952629 91.202538 86.983772 82.994164
## [9561] 194.716492 40.313316 174.253815 169.193192 169.669739
## [9566] 139.822037 99.243225 147.841156 129.072235 134.528305
## [9571] 74.275497 174.161179 86.806061 262.494080 89.050415
## [9576] 111.483116 78.880806 104.085030 195.311920 72.853851
## [9581] 195.332809 154.209991 157.914047 235.277802 189.285751
## [9586] 113.478264 74.473656 241.567062 120.808044 178.912766
## [9591] 114.223572 142.666046 160.088058 133.395676 128.867111
## [9596] 140.332764 125.710022 136.486130 93.581161 136.910141
## [9601] 150.110626 196.388016 85.101624 214.920456 95.857033
## [9606] 208.425491 174.038971 179.382034 122.108604 56.527874
## [9611] 180.709991 153.121399 63.319183 108.992439 63.927273
## [9616] 194.610565 113.663528 58.663151 110.267365 91.568291
## [9621] 59.692974 118.060364 130.503601 51.388844 206.112549
## [9626] 112.049202 258.367828 116.216888 151.665527 110.140457
## [9631] 84.731331 98.334312 175.542755 119.880775 164.772690
## [9636] 79.580544 103.833260 127.398705 114.095428 181.671600
## [9641] 258.177734 214.251831 163.816757 236.802597 64.746887
## [9646] 296.279053 104.659843 71.878700 168.671829 97.533272
## [9651] 109.521896 521.002136 270.047882 154.576981 215.400589
## [9656] 187.070450 135.638367 69.945091 189.035202 72.484169
## [9661] 78.436646 87.069397 117.145058 103.361565 100.994469
## [9666] 72.742226 84.559784 124.125648 120.270660 193.847443
## [9671] 130.575638 96.996132 119.907806 134.126129 114.174530
## [9676] 125.003799 106.064957 136.608658 101.025475 132.338745
## [9681] 58.788540 131.223053 158.705994 103.306152 117.343552
## [9686] 98.215805 152.087219 170.120941 242.927292 83.919662
## [9691] 139.853241 259.618195 371.754791 46.509224 197.064804
## [9696] 183.871063 136.756119 128.187973 121.618118 169.671600
## [9701] 346.342682 74.485420 305.228027 199.972305 84.841621
## [9706] 119.337700 98.051193 242.267395 218.500687 153.088394
## [9711] 171.818283 136.350952 194.872330 148.786407 195.900925
## [9716] 214.236053 145.249084 174.695831 165.775391 218.522568
## [9721] 188.432373 144.968201 229.734497 103.483971 212.992523
## [9726] 313.530365 82.062355 138.284744 88.600235 87.440575
## [9731] 55.585869 111.006325 241.239090 114.412720 120.960999
## [9736] 99.915642 97.972115 128.625580 110.134163 86.202812
## [9741] 212.262421 98.698654 65.653748 38.161533 84.678574
## [9746] -29.405849 178.022827 64.222694 50.388779 110.607391
## [9751] 140.066330 114.830956 81.241882 161.039642 104.211235
## [9756] 102.627495 150.556641 248.806152 -30.547649 298.229309
## [9761] 159.680771 197.523209 -27.754459 66.798225 85.254799
## [9766] 133.779617 165.278625 207.641891 -59.277031 159.529175
## [9771] -43.773167 145.670273 102.049911 82.593529 174.503189
## [9776] 280.172241 138.242493 131.005493 173.387054Wordcloud
Description= data1$summary
data2<-Corpus(VectorSource(Description))
data2<-tm_map(data2,stripWhitespace)## Warning in tm_map.SimpleCorpus(data2, stripWhitespace): transformation
## drops documentsdata2<-tm_map(data2,tolower)## Warning in tm_map.SimpleCorpus(data2, tolower): transformation drops
## documentsdata2<-tm_map(data2,removeNumbers)## Warning in tm_map.SimpleCorpus(data2, removeNumbers): transformation drops
## documentsdata2<-tm_map(data2,removePunctuation)## Warning in tm_map.SimpleCorpus(data2, removePunctuation): transformation
## drops documentsdata2<-tm_map(data2,removeWords, stopwords('english'))## Warning in tm_map.SimpleCorpus(data2, removeWords, stopwords("english")):
## transformation drops documentsdata2<-tm_map(data2,removeWords, c('and','the','our','that','for','are','also','more','has','must','have','should','this','with'))## Warning in tm_map.SimpleCorpus(data2, removeWords, c("and", "the", "our", :
## transformation drops documentswordcloud(data2, scale=c(3,0.5), max.words=1000,
random.order=FALSE, rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8,'Dark2'))## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : roommates could not be fit on page. It will not be
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## plotted.
Conclusion: It can be concluded from the above analysis that When the four neighbourhoods are compared, we can conclude that Manhattan and Brooklyn have higher number of properties listed and have higher number of zipcodes when compared to Queens and Staten Island.
We also observe a huge difference in price per night for properties in Manhattan and have a few outliers. Queens and Staten Island in contrast have a consistent price per night for the properties listed.
Zipcodes in Manhattan have properties with the highest price per night asking.
The mean property cost of properties in Manhattan is very high, and so is the price per night of stay. Hence this neighbourhood has the highest breakeven period.
We observe that Staten Island zipcodes have lower mean cost of properties, but fairly high price per night making it the most profitable neighbourhoods.
We can also conclude from our analysis that Brooklyn zipcodes are doing a pretty decent job in terms of breakeven period. Properties have average cost in this zipcodes and the price per night on Airbnb also are neither too high nor too low
Our sentimental analysis tells us whether the summary provided by the owner for a room/apartment appeals the customers in a positive or a negative way. The word cloud shows us the intensity of the words that tells us about the word frequency. So, in this case we have made word clouds for the positive as well as the negative summary. This would help the owners to change their way towards the marketing side, so that they can use the most used positive words in order to increase their customer staying at their place resulting in more revenue for themselves